Overview
This is the fourth blog post of my Finaical Analysis in Python series.
This blog series covers topics like the following:
- how to visualize the long-term investment plan
- backtest dollar-cost averaging strategy for long-term investment
- backtest value averaging strategy for long-term investment
- compare different investment strategies for short-term investment
The jupyter notebooks can be downloaded here.
Disclaimer
Investing money into anything is often involved with risk. Please do your own research before investing and be responsible for your own investment decisions.
I am just learning investment on my own and want to share some codes that I have written that might be useful for others.
The content here is only for informational purpose (instead of taking them as professional investment advice).
Introduction
Imagine that we have a fixed amount of initial investment and we want to set up a short-term investment plan.
We can either decide to invest all at once into the stock market at the beginning of each year, or apply the dollar-cost averaging or value averaging on a monthly or quarterly basis.
Which method could maximize our investment return?
Let’s do some backtests using the SP500 index performance data.
# Import libraries
import datetime
from matplotlib import pyplot as plt
import pandas as pd
from pandas_datareader import data as pdr
plt.style.use('bmh')
# Get historical data
startdate = datetime.datetime(1988, 1, 1)
enddate = datetime.datetime(2021, 1, 1)
# SP 500 total return index
SP = pdr.get_data_yahoo('^SP500TR', start=startdate, end=enddate)
SP['date'] = SP.index
Compare all-in with dollar cost averaging
Let’s first assume that the timeframe for the short-term investment plan is one year and the investment should start at the beginning of each year. Hence. each year between 1980 and 2020 is an individual sample.
In this section, I will compare the annual return rates for these investment plans:
- Plan 1: All-in approach at the beginning of a year
- Plan 2: Divide the initial investment into 12 folds and invest at the beginning of each month
- Plan 3: Divide the initial investment into 4 folds and invest at the beginning of each quarter
# Plan 1: All-in
# Group by year
SP['date'] = SP.index
SP['year'] = SP['date'].apply(lambda d: f'{d.year}')
df = SP[['year', 'Adj Close']].groupby('year').first()
df['return'] = df['Adj Close'].pct_change()
df['return_next_y'] = df['return'].shift(-1)
# Get a feeling of the return rate per year
df['return_next_y'].hist(bins=20);
# Plan 2: Monthly dollar-cost averaging
# Group by month
SP['month'] = SP['date'].apply(lambda d: f'{d.month:02d}')
df_m = SP[['year', 'month', 'Adj Close']].groupby(['year','month']).first()
df_m['return'] = df_m['Adj Close'].pct_change()
df_m['return_next'] = df_m['return'].shift(-1)
# Plan 3: Quarterly dollar-cost averaging
# Group by quarter
SP['quarter'] = SP['month'].apply(lambda m: int(m)//4 + 1)
df_q = SP[['year', 'quarter', 'Adj Close']].groupby(['year','quarter']).first()
df_q['return'] = df_q['Adj Close'].pct_change()
df_q['return_next'] = df_q['return'].shift(-1)
Here is how the dataframe for the quarterly dollar-cost averaging looks like:
df_q.head()
| year | quarter | Adj Close | return | return_next |
|---|---|---|---|---|
| 1988 | 1 | 256.019989 | NaN | 0.009335 |
| 2 | 258.410004 | 0.009335 | 0.074881 | |
| 3 | 277.760010 | 0.074881 | 0.015085 | |
| 4 | 281.950012 | 0.015085 | 0.013123 | |
| 1989 | 1 | 285.649994 | 0.013123 | 0.085979 |
# Function for calcuting dollar-cost averaging returns
def calc_return_over_periods_list(
investment_per_period_list: list,
return_per_period_list: list,
n_period: int,
print_values: bool = False) -> float:
"""
This function calculates the overall rate of return of the dollar-cost averaging strategy.
:param investment_per_period_list: Investment value at the beginning of each period.
:param return_per_period_list: Historical return rate per period.
:param n_period: number of periods for calculating the overall return rate.
:param print_values: Whether to display the value each period and overall return rate or not
:return: A dataframe that contains all the relevant info
"""
value = 0
for i in range(n_period):
value += investment_per_period_list[i]
value = value * (1 + return_per_period_list[i])
if print_values:
print(f'Peorid {i}: value = {round(value, 2)}')
return_rate = value/sum(investment_per_period_list) - 1
if print_values:
print(f'The overall return rate is {round(return_rate*100,2)}%')
return return_rate
Here is how the function works for calculating the overall return for a dollar-cost averaging strategy:
# Assuming investing 300 dollars at the beginning of each quarter
calc_return_over_periods_list(
investment_per_period_list = [300]*4,
return_per_period_list = df_q.loc['2019','return_next'].tolist(),
n_period=4,
print_values=True)
Output:
Peorid 0: value = 344.45
Peorid 1: value = 668.05
Peorid 2: value = 1027.87
Peorid 3: value = 1391.54
The overall return rate is 15.96%.
# Join dollar-cost averaging results together into one dataframe
df = df.drop(columns='return')
for year in SP.year.unique()[:-1]:
# for monthly dollar-cost averaging annual return
df.loc[f'{int(year)}', 'return_next_m'] = (
calc_return_over_periods_list(
[1]*12,
df_m.loc[(f'{year}'),'return_next'].tolist(),
12)
)
# for quarterly dollar-cost averaging annual return
df.loc[f'{int(year)}', 'return_next_q'] = (
calc_return_over_periods_list(
[1]*4,
df_q.loc[(f'{year}'),'return_next'].tolist(),
4)
)
df.round(3)
| year | Adj Close | return_next_y | return_next_m | return_next_q |
|---|---|---|---|---|
| 1988 | 256.02 | 0.116 | 0.059 | 0.066 |
| 1989 | 285.65 | 0.352 | 0.152 | 0.172 |
| 1990 | 386.16 | -0.059 | -0.008 | -0.031 |
| 1991 | 363.44 | 0.320 | 0.137 | 0.165 |
| 1992 | 479.84 | 0.075 | 0.064 | 0.057 |
| 1993 | 516.00 | 0.099 | 0.047 | 0.053 |
| 1994 | 567.10 | 0.015 | 0.015 | 0.029 |
| 1995 | 575.52 | 0.387 | 0.188 | 0.198 |
| 1996 | 798.22 | 0.214 | 0.121 | 0.120 |
| 1997 | 969.05 | 0.347 | 0.151 | 0.172 |
| 1998 | 1305.04 | 0.279 | 0.159 | 0.139 |
| 1999 | 1668.52 | 0.200 | 0.118 | 0.120 |
| 2000 | 2002.11 | -0.108 | -0.101 | -0.094 |
| 2001 | 1785.86 | -0.088 | -0.026 | -0.023 |
| 2002 | 1628.51 | -0.200 | -0.078 | -0.097 |
| 2003 | 1303.17 | 0.242 | 0.179 | 0.182 |
| 2004 | 1618.05 | 0.103 | 0.074 | 0.071 |
| 2005 | 1784.96 | 0.075 | 0.062 | 0.053 |
| 2006 | 1918.96 | 0.138 | 0.096 | 0.096 |
| 2007 | 2183.92 | 0.041 | -0.008 | 0.013 |
| 2008 | 2273.41 | -0.341 | -0.229 | -0.189 |
| 2009 | 1499.17 | 0.245 | 0.249 | 0.207 |
| 2010 | 1867.06 | 0.145 | 0.141 | 0.109 |
| 2011 | 2138.30 | 0.025 | 0.018 | 0.007 |
| 2012 | 2192.40 | 0.172 | 0.079 | 0.084 |
| 2013 | 2568.55 | 0.280 | 0.145 | 0.143 |
| 2014 | 3286.69 | 0.147 | 0.087 | 0.085 |
| 2015 | 3768.68 | -0.001 | -0.009 | -0.020 |
| 2016 | 3763.99 | 0.146 | 0.094 | 0.084 |
| 2017 | 4315.08 | 0.218 | 0.121 | 0.124 |
| 2018 | 5256.28 | -0.051 | -0.078 | -0.066 |
| 2019 | 4990.56 | 0.324 | 0.150 | 0.160 |
| 2020 | 6609.29 | NaN | NaN | NaN |
With the dataframe above, we can easily compare the performance of these three short-term investment plans:
# Return_next_y is the annual return for all-in method
# Return_next_m is the annual return of the monthly dollar cost averaging method
# Return_next_q is the annual return of the quarterly dollar cost averaging method
df[['return_next_y','return_next_m','return_next_q']].describe().round(3)
| return_next_y | return_next_m | return_next_q | |
|---|---|---|---|
| count | 32.000 | 32.000 | 32.000 |
| mean | 0.121 | 0.068 | 0.068 |
| std | 0.169 | 0.099 | 0.096 |
| min | -0.341 | -0.229 | -0.189 |
| 25% | 0.023 | 0.009 | 0.012 |
| 50% | 0.142 | 0.083 | 0.084 |
| 75% | 0.243 | 0.142 | 0.140 |
| max | 0.387 | 0.249 | 0.207 |
plt.figure(figsize=(15,5))
plt.plot(df['return_next_y'], label='all-in')
plt.plot(df['return_next_m'], label='dollar-cost (monthly)')
plt.plot(df['return_next_q'], label='dollar-cost (quarterly)')
plt.axhline(y=0, color='r', linestyle='-')
plt.xlabel('year')
plt.ylabel('return rate')
plt.xticks(rotation=90)
plt.title('Annual return rate of the all-in vs. dollar-cost average methods')
plt.legend()
plt.show()
Observation
Given that you already have a fixed amount of initial investment and you want to invest it into an ETF based on SP500, all-in method performed better than both monthly and quarterly dollar-cost averaging methods in most of the past years.
This is because SP500 has a tendency of increasing in most of the past years; and the all-in method maximized the gain.
However, dollar-cost averaging method helps to reduce loss in a bear market.
It seems that only in the following years SP500 has a negative year-over-year return.
list(df.loc[df['return_next_y']<=0,:].index)
['1990', '2000', '2001', '2002', '2008', '2015', '2018']
The bear markets does not seem to last long using the SP 500 index (usually less than 2 years).
So, it might be a good time to start investing if the last year was a bear market.
If it is already a bull market, you can decide whether you still want to risk it with the all-in method.
If it is already a bear market, it might be a good time to start investing.
Compare with value averaging
In the last notebook, we already discussed the method of value averaging for long term investment, and we assumed that it might work for short term investment.
Let’s check the annual return of the value averaging method here:
# This is an adjusted version of the function that we have introduced in the last notebook for value averaging
def calc_value_averaging_return(df: pd.DataFrame, value_per_period: float = 1.0,
increase_investment_per_period: float = 0.0) -> float:
"""
This function calculates the overall return rate of the value averaging strategy.
:param df: Original dataframe that contains the price at the beginning of each period
:param value_per_period: Investment value per period
:param increase_investment_per_period: Increase the investment by x each period
:return: overall return rate (might be negative if the total withdraw amount is too much)
"""
df_va = pd.DataFrame.copy(df)
value = value_per_period
df_va.loc[0,'target_value'] = value
df_va.loc[0,'shares_should_have_in_total'] = value_per_period/df_va.loc[0,'Adj Close']
df_va.loc[0,'shares_to_buy_or_sell'] = value_per_period/df_va.loc[0,'Adj Close']
df_va.loc[0,'should_invest_or_withdraw'] = value
df_va.loc[0,'total_invest_or_withdraw'] = value
for i in range(1,len(df_va.index)):
value += value_per_period*((1 + increase_investment_per_period)**i)
df_va.loc[i,'target_value'] = value
df_va.loc[i,'shares_should_have_in_total'] = df_va.loc[i,'target_value']/df_va.loc[i,'Adj Close']
df_va.loc[i,'shares_to_buy_or_sell'] = df_va.loc[i,'shares_should_have_in_total'] - df_va.loc[i-1,'shares_should_have_in_total']
df_va.loc[i,'should_invest_or_withdraw'] = df_va.loc[i,'shares_to_buy_or_sell']*df_va.loc[i,'Adj Close']
df_va.loc[i,'total_invest_or_withdraw'] = df_va.loc[i-1,'total_invest_or_withdraw'] + df_va.loc[i,'should_invest_or_withdraw']
return df_va.loc[len(df_va.index)-1,'target_value']/df_va.loc[len(df_va.index)-1,'total_invest_or_withdraw']-1
# Assuming fixed investment value each year
value_per_year = 1200
value_per_quarter = value_per_year/4
value_per_month = value_per_year/12
df_va_m = df_m['Adj Close'].reset_index()
df_va_q = df_q['Adj Close'].reset_index()
### Join results together into one dataframe
for year in SP.year.unique()[:-1]:
df_va_m_sample = df_va_m.loc[df_va_m['year']==year,:].reset_index()
df_va_q_sample = df_va_q.loc[df_va_q['year']==year,:].reset_index()
annual_return_va_m = calc_value_averaging_return(df_va_m_sample, value_per_month)
annual_return_va_q = calc_value_averaging_return(df_va_q_sample, value_per_quarter)
df.loc[f'{int(year)}', 'return_va_m'] = annual_return_va_m
df.loc[f'{int(year)}', 'return_va_q'] = annual_return_va_q
# Join value averaging with dollar cost averaging results
df.round(3)
| year | Adj Close | return_next_y | return_next_m | return_next_q | return_va_m | return_va_q |
|---|---|---|---|---|---|---|
| 1988 | 256.02 | 0.116 | 0.059 | 0.066 | 0.049 | 0.054 |
| 1989 | 285.65 | 0.352 | 0.152 | 0.172 | 0.128 | 0.152 |
| 1990 | 386.16 | -0.059 | -0.008 | -0.031 | -0.011 | -0.037 |
| 1991 | 363.44 | 0.320 | 0.137 | 0.165 | 0.037 | 0.065 |
| 1992 | 479.84 | 0.075 | 0.064 | 0.057 | 0.052 | 0.045 |
| 1993 | 516.00 | 0.099 | 0.047 | 0.053 | 0.038 | 0.044 |
| 1994 | 567.10 | 0.015 | 0.015 | 0.029 | -0.008 | 0.005 |
| 1995 | 575.52 | 0.387 | 0.188 | 0.198 | 0.171 | 0.188 |
| 1996 | 798.22 | 0.214 | 0.121 | 0.120 | 0.165 | 0.170 |
| 1997 | 969.05 | 0.347 | 0.151 | 0.172 | 0.161 | 0.194 |
| 1998 | 1305.04 | 0.279 | 0.159 | 0.139 | 0.128 | 0.094 |
| 1999 | 1668.52 | 0.200 | 0.118 | 0.120 | 0.080 | 0.078 |
| 2000 | 2002.11 | -0.108 | -0.101 | -0.094 | -0.072 | -0.068 |
| 2001 | 1785.86 | -0.088 | -0.026 | -0.023 | -0.040 | -0.041 |
| 2002 | 1628.51 | -0.200 | -0.078 | -0.097 | -0.052 | -0.062 |
| 2003 | 1303.17 | 0.242 | 0.179 | 0.182 | 0.145 | 0.156 |
| 2004 | 1618.05 | 0.103 | 0.074 | 0.071 | 0.067 | 0.064 |
| 2005 | 1784.96 | 0.075 | 0.062 | 0.053 | 0.062 | 0.050 |
| 2006 | 1918.96 | 0.138 | 0.096 | 0.096 | 0.083 | 0.086 |
| 2007 | 2183.92 | 0.041 | -0.008 | 0.013 | 0.009 | 0.029 |
| 2008 | 2273.41 | -0.341 | -0.229 | -0.189 | -0.275 | -0.236 |
| 2009 | 1499.17 | 0.245 | 0.249 | 0.207 | 0.255 | 0.216 |
| 2010 | 1867.06 | 0.145 | 0.141 | 0.109 | 0.093 | 0.054 |
| 2011 | 2138.30 | 0.025 | 0.018 | 0.007 | -0.001 | -0.019 |
| 2012 | 2192.40 | 0.172 | 0.079 | 0.084 | 0.041 | 0.044 |
| 2013 | 2568.55 | 0.280 | 0.145 | 0.143 | 0.134 | 0.131 |
| 2014 | 3286.69 | 0.147 | 0.087 | 0.085 | 0.088 | 0.085 |
| 2015 | 3768.68 | -0.001 | -0.009 | -0.020 | 0.040 | 0.022 |
| 2016 | 3763.99 | 0.146 | 0.094 | 0.084 | 0.063 | 0.052 |
| 2017 | 4315.08 | 0.218 | 0.121 | 0.124 | 0.102 | 0.107 |
| 2018 | 5256.28 | -0.051 | -0.078 | -0.066 | 0.027 | 0.039 |
| 2019 | 4990.56 | 0.324 | 0.150 | 0.160 | 0.104 | 0.113 |
| 2020 | 6609.29 | NaN | NaN | NaN | NaN | NaN |
plt.figure(figsize=(15,5))
plt.plot(df['return_next_y'], label='all-in')
plt.plot(df['return_va_m'], label='value-averaging (monthly)')
# plt.plot(df['return_va_q'], label='value-averaging (quarterly)')
plt.plot(df['return_next_m'], label='dollar-cost (monthly)')
# plt.plot(df['return_next_q'], label='dollar-cost (quarterly)')
plt.axhline(y=0, color='r', linestyle='-')
plt.xlabel('year')
plt.ylabel('return rate')
plt.xticks(rotation=90)
plt.title('Annual return rate of the all-in, monthly dollar-cost averaging, and monthly value averaging methods')
plt.legend()
plt.show()
def compare_strategy(df, str1, str2, str1_name, str2_name):
print(f'number of times {str1_name} is better than {str2_name}: {df.loc[df[str1] > df[str2],:].shape[0]}')
print(f'number of times {str1_name} is equal to {str2_name}: {df.loc[df[str1] == df[str2],:].shape[0]}')
print(f'number of times {str1_name} is worse than {str2_name}: {df.loc[df[str1] < df[str2],:].shape[0]}')
compare_strategy(df, 'return_va_m', 'return_next_m', 'monthly value-averaging', 'monthly dollar-cost-averaging')
Output:
number of times monthly value-averaging is better than monthly dollar-cost-averaging: 9
number of times monthly value-averaging is equal to monthly dollar-cost-averaging: 0
number of times monthly value-averaging is worse than monthly dollar-cost-averaging: 23
compare_strategy(df, 'return_next_y', 'return_next_m', 'all-in', 'monthly dollar-cost-averaging')
Output:
number of times all-in is better than monthly dollar-cost-averaging: 26
number of times all-in is equal to monthly dollar-cost-averaging: 0
number of times all-in is worse than monthly dollar-cost-averaging: 6
Observation
It seems that the annual return of the dollar-cost averaging and value averaging did not differ much, even though the monthly value averaging method was worse than the monthly dollar-cost-averaging in most years.
Again, all-in method brings the most gain but also involves the most risk.
What if we use a multi-year timeframe?
The test below involves a multiple year short-term investment plan. The rate of return below indicates the overall rate of return of investing for x years starting from a certain year.
Let’s see if using dollar cost averaging or value averaging helps to improve the peformance.
SP
| Date | High | Low | Open | Close | Volume | Adj Close | date | year | month | quarter |
|---|---|---|---|---|---|---|---|---|---|---|
| 1988-01-04 | 256.019989 | 256.019989 | 256.019989 | 256.019989 | 0 | 256.019989 | 1988-01-04 | 1988 | 01 | 1 |
| 1988-01-05 | 258.769989 | 258.769989 | 258.769989 | 258.769989 | 0 | 258.769989 | 1988-01-05 | 1988 | 01 | 1 |
| 1988-01-06 | 259.029999 | 259.029999 | 259.029999 | 259.029999 | 0 | 259.029999 | 1988-01-06 | 1988 | 01 | 1 |
| 1988-01-07 | 261.209991 | 261.209991 | 261.209991 | 261.209991 | 0 | 261.209991 | 1988-01-07 | 1988 | 01 | 1 |
| 1988-01-08 | 243.550003 | 243.550003 | 243.550003 | 243.550003 | 0 | 243.550003 | 1988-01-08 | 1988 | 01 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2020-09-21 | 6757.350098 | 6641.330078 | 6757.350098 | 6748.080078 | 0 | 6748.080078 | 2020-09-21 | 2020 | 09 | 3 |
| 2020-09-22 | 6828.669922 | 6727.319824 | 6778.330078 | 6819.080078 | 0 | 6819.080078 | 2020-09-22 | 2020 | 09 | 3 |
| 2020-09-23 | 6835.209961 | 6649.240234 | 6828.919922 | 6657.819824 | 0 | 6657.819824 | 2020-09-23 | 2020 | 09 | 3 |
| 2020-09-24 | 6744.000000 | 6602.359863 | 6635.979980 | 6678.040039 | 0 | 6678.040039 | 2020-09-24 | 2020 | 09 | 3 |
| 2020-09-25 | 6802.140137 | 6642.040039 | 6657.830078 | 6784.950195 | 0 | 6784.950195 | 2020-09-25 | 2020 | 09 | 3 |
8250 rows 10 columns
def compare_returns(SP, n_years):
"""
This function aims to compare 5 n-year short-term investment plans based on a certain ETF/stock's performance data.
:param SP: Original dataframe that contains a certain ETF/stock's performance data
:param n_years: Number of years set for the short-term investment plan
:return: A dataframe that contains the overall return for the 5 plans
"""
test_months = 12*n_years
test_quarters = 4*n_years
df = SP[['year', 'Adj Close']].groupby('year').first()
# Compute all in return
df['return'] = df['Adj Close'].pct_change(periods=n_years)
df['return_next'] = df['return'].shift(-n_years)
df = df.drop(columns='return')
for year in SP.year.unique()[:-n_years]:
# Compute monthly dollar cost averaging return
df.loc[f'{int(year)}', 'return_next_m'] = (
_calc_return_over_periods_list_simplified(
[1]*test_months,
df_m.loc[(f'{year}'):(f'{int(year) + n_years - 1}'),'return_next'].tolist(),
test_months)
)
# Compute quarterly dollar cost averaging return
df.loc[f'{int(year)}', 'return_next_q'] = (
_calc_return_over_periods_list_simplified(
[1]*test_quarters,
df_q.loc[(f'{year}'):(f'{int(year) + n_years - 1}'),'return_next'].tolist(),
test_quarters)
)
# Compute value averaging returns
df_va_m = df_m['Adj Close'].reset_index()
df_va_q = df_q['Adj Close'].reset_index()
### Join results together into one dataframe
for year in SP.year.unique()[:-n_years]:
df_va_m_sample = df_va_m.loc[(df_va_m['year']>=year) &
(df_va_m['year']<=str(int(year) + n_years - 1)),:].reset_index()
df_va_q_sample = df_va_q.loc[(df_va_q['year']>=year) &
(df_va_q['year']<=str(int(year) + n_years - 1)),:].reset_index()
annual_return_va_m = _calc_value_averaging_return_simplified(df_va_m_sample)
annual_return_va_q = _calc_value_averaging_return_simplified(df_va_q_sample)
df.loc[f'{int(year)}', 'return_va_m'] = annual_return_va_m
df.loc[f'{int(year)}', 'return_va_q'] = annual_return_va_q
return df
def _calc_return_over_periods_list_simplified(
investment_per_period_list: list,
return_per_period_list: list,
n_period: int,
print_values=False) -> float:
value = 0
for i in range(n_period):
value += investment_per_period_list[i]
value = value * (1 + return_per_period_list[i])
return_rate = value/sum(investment_per_period_list) - 1
return return_rate
def _calc_value_averaging_return_simplified(df_va, value_per_period=1):
df_va.loc[0,'total_value'] = value_per_period
df_va.loc[0,'shares_should_have_in_total'] = value_per_period/df_va.loc[0,'Adj Close']
df_va.loc[0,'shares_to_buy_or_sell'] = value_per_period/df_va.loc[0,'Adj Close']
df_va.loc[0,'should_invest'] = value_per_period
df_va.loc[0,'total_invest'] = value_per_period
for i in range(1,len(df_va.index)):
df_va.loc[i,'total_value'] = value_per_period*(i+1)
df_va.loc[i,'shares_should_have_in_total'] = df_va.loc[i,'total_value']/df_va.loc[i,'Adj Close']
df_va.loc[i,'shares_to_buy_or_sell'] = df_va.loc[i,'shares_should_have_in_total'] - df_va.loc[i-1,'shares_should_have_in_total']
df_va.loc[i,'should_invest'] = df_va.loc[i,'shares_to_buy_or_sell']*df_va.loc[i,'Adj Close']
df_va.loc[i,'total_invest'] = df_va.loc[i-1,'total_invest'] + df_va.loc[i,'should_invest']
return df_va.loc[len(df_va.index)-1,'total_value']/df_va.loc[len(df_va.index)-1,'total_invest']-1
def plot_changes(df):
plt.figure(figsize=(15,5))
plt.plot(df['return_next'], label='all-in')
plt.plot(df['return_va_m'], label='value-averaging (monthly)')
# plt.plot(df['return_va_q'], label='value-averaging (quarterly)')
plt.plot(df['return_next_m'], label='dollar-cost (monthly)')
# plt.plot(df['return_next_q'], label='dollar-cost (quarterly)')
plt.axhline(y=0, color='r', linestyle='-')
plt.xlabel('year')
plt.ylabel('return rate')
plt.xticks(rotation=90)
plt.title('Annual return rate of the all-in, monthly dollar-cost averaging, and monthly value averaging methods')
plt.legend()
plt.show()
# Assuming we are setting up a 2 year plan and then compare the return
test_years = 2
df_2 = compare_returns(SP, test_years)
df_2.round(3)
| year | Adj Close | return_next | return_next_m | return_next_q | return_va_m | return_va_q |
|---|---|---|---|---|---|---|
| 1988 | 256.02 | 0.508 | 0.291 | 0.306 | 0.296 | 0.322 |
| 1989 | 285.65 | 0.272 | 0.038 | 0.036 | 0.039 | 0.030 |
| 1990 | 386.16 | 0.243 | 0.223 | 0.222 | 0.127 | 0.130 |
| 1991 | 363.44 | 0.420 | 0.143 | 0.155 | 0.138 | 0.152 |
| 1992 | 479.84 | 0.182 | 0.108 | 0.107 | 0.102 | 0.102 |
| 1993 | 516.00 | 0.115 | 0.039 | 0.049 | 0.017 | 0.026 |
| 1994 | 567.10 | 0.408 | 0.298 | 0.313 | 0.308 | 0.340 |
| 1995 | 575.52 | 0.684 | 0.281 | 0.287 | 0.367 | 0.390 |
| 1996 | 798.22 | 0.635 | 0.330 | 0.340 | 0.393 | 0.437 |
| 1997 | 969.05 | 0.722 | 0.315 | 0.318 | 0.312 | 0.300 |
| 1998 | 1305.04 | 0.534 | 0.254 | 0.243 | 0.239 | 0.212 |
| 1999 | 1668.52 | 0.070 | -0.052 | -0.048 | -0.020 | -0.021 |
| 2000 | 2002.11 | -0.187 | -0.103 | -0.098 | -0.105 | -0.109 |
| 2001 | 1785.86 | -0.270 | -0.149 | -0.157 | -0.114 | -0.112 |
| 2002 | 1628.51 | -0.006 | 0.161 | 0.152 | 0.133 | 0.136 |
| 2003 | 1303.17 | 0.370 | 0.187 | 0.187 | 0.189 | 0.194 |
| 2004 | 1618.05 | 0.186 | 0.109 | 0.102 | 0.113 | 0.104 |
| 2005 | 1784.96 | 0.224 | 0.153 | 0.147 | 0.146 | 0.144 |
| 2006 | 1918.96 | 0.185 | 0.066 | 0.077 | 0.088 | 0.099 |
| 2007 | 2183.92 | -0.314 | -0.288 | -0.260 | -0.310 | -0.277 |
| 2008 | 2273.41 | -0.179 | 0.104 | 0.109 | 0.116 | 0.143 |
| 2009 | 1499.17 | 0.426 | 0.286 | 0.246 | 0.258 | 0.204 |
| 2010 | 1867.06 | 0.174 | 0.094 | 0.072 | 0.082 | 0.046 |
| 2011 | 2138.30 | 0.201 | 0.136 | 0.132 | 0.108 | 0.097 |
| 2012 | 2192.40 | 0.499 | 0.263 | 0.265 | 0.275 | 0.278 |
| 2013 | 2568.55 | 0.467 | 0.200 | 0.198 | 0.217 | 0.212 |
| 2014 | 3286.69 | 0.145 | 0.038 | 0.032 | 0.095 | 0.078 |
| 2015 | 3768.68 | 0.145 | 0.115 | 0.104 | 0.091 | 0.074 |
| 2016 | 3763.99 | 0.396 | 0.227 | 0.222 | 0.222 | 0.219 |
| 2017 | 4315.08 | 0.157 | -0.007 | 0.001 | 0.111 | 0.120 |
| 2018 | 5256.28 | 0.257 | 0.185 | 0.199 | 0.154 | 0.168 |
| 2019 | 4990.56 | NaN | NaN | NaN | NaN | NaN |
| 2020 | 6609.29 | NaN | NaN | NaN | NaN | NaN |
plot_changes(df_2)
compare_strategy(df_2, 'return_va_m', 'return_next_m', 'monthly value-averaging', 'monthly dollar-cost-averaging')
Output:
number of times monthly value-averaging is better than monthly dollar-cost-averaging: 15
number of times monthly value-averaging is equal to monthly dollar-cost-averaging: 0
number of times monthly value-averaging is worse than monthly dollar-cost-averaging: 16
compare_strategy(df_2, 'return_next', 'return_next_m', 'all-in', 'monthly dollar-cost-averaging')
Output:
number of times all-in is better than monthly dollar-cost-averaging: 26
number of times all-in is equal to monthly dollar-cost-averaging: 0
number of times all-in is worse than monthly dollar-cost-averaging: 5
# Assuming we are setting up a 3 year plan and then compare the return
test_years = 3
df_3 = compare_returns(SP, test_years)
plot_changes(df_3)
compare_strategy(df_3, 'return_va_m', 'return_next_m', 'monthly value-averaging', 'monthly dollar-cost-averaging')
Output:
number of times monthly value-averaging is better than monthly dollar-cost-averaging: 22
number of times monthly value-averaging is equal to monthly dollar-cost-averaging: 0
number of times monthly value-averaging is worse than monthly dollar-cost-averaging: 8
compare_strategy(df_3, 'return_next', 'return_next_m', 'all-in', 'monthly dollar-cost-averaging')
Output:
number of times all-in is better than monthly dollar-cost-averaging: 25
number of times all-in is equal to monthly dollar-cost-averaging: 0
number of times all-in is worse than monthly dollar-cost-averaging: 5
# Assuming we are setting up a 4 year plan and then compare the return
test_years = 4
df_4 = compare_returns(SP, test_years)
plot_changes(df_4)
compare_strategy(df_4, 'return_va_m', 'return_next_m', 'monthly value-averaging', 'monthly dollar-cost-averaging')
Output:
number of times monthly value-averaging is better than monthly dollar-cost-averaging: 24
number of times monthly value-averaging is equal to monthly dollar-cost-averaging: 0
number of times monthly value-averaging is worse than monthly dollar-cost-averaging: 5
compare_strategy(df_4, 'return_next', 'return_next_m', 'all-in', 'monthly dollar-cost-averaging')
Output:
number of times all-in is better than monthly dollar-cost-averaging: 23
number of times all-in is equal to monthly dollar-cost-averaging: 0
number of times all-in is worse than monthly dollar-cost-averaging: 6
Conclusion
When we have a fixed amount of money, in most cases, using the all-in method performs the best, but it also involves higher risk.
The value averaging strategy did not differ much from the dollar cost averaging. When it is a one or two year short investment plan, the dollar cost averaging is more likely to perform better; if it is a three or four year investment plan, the value averaging strategy is more likely to perform better.
Given that the value averaging strategy is a bit tedious to implement, I think it is better to use either all-in or dollar-cost averaging for short term investment.
Please note that, in this notebook, I did not use the adjusted version of the value averaging method with the minimum and maximum thresholds of investment per period (as introduced in BP3 Plan 3). It might bring you some extra return if it is a longer term investment plan.


{% extends 'email_base.html' %} {% load i18n %} {% block body %}
{% blocktrans %}Hi there {{studygroup.facilitator.first_name}}{% endblocktrans %},
{% blocktrans %}We saw that you created a Learning Circle last week. Amazing!{% endblocktrans %}
{% blocktrans %}The Getting Started section of the handbook has lots of tips on how to promote your Learning Circle in your community. Once this is complete, you can check out the Running Learning Circles section to read facilitation tips and tricks from past facilitators.{% endblocktrans %}
{% blocktrans %}If you have any other questions, or are looking for general support, post your questions in our facilitator community.{% endblocktrans %}
{% trans "Cheers" %},
P2PU


Issue a claim
Pages
- Create an account
- Your firm address
- Your firm contact details
- Your reference for this claim
- Choose a county court
- Choose claimant type
- Claimant Address
- Choose defendant type
- Defendant Address
- Defendant Service address
- Summarise the claim
- Type of claim
- Enter claim value
- Is it a personal injury claim?
- Is it a claim for housing disrepair?
- Calculating your fee
- Check the claim details
- Do you want to download a draft of the claim form for your client to sign?
- Statement of truth
- Choose how to pay
- Pay by Fee Account
- Pay by credit card
- Your claim has been issued
Sprint 3 - Prototype June 2017
Pages
- Create an account
- Your firm address
- Your firm contact details
- Your reference for this claim
- Choose claimant type
- Claimant Address
- Choose defendant type
- Defendant Address
- Defendant Service address
- Summarise the claim
- How do you intend to serve particulars of claim?
- Type of claim
- Enter claim value
- Do you want to claim interest?
- Is it a personal injury claim?
- Is it a claim for housing disrepair?
- Calculating your fee
- Check the claim details
- Do you want to download a draft of the claim form for your client to sign?
- Statement of truth
- Choose how to pay
- Pay by Fee Account
- Pay by credit card
- Your claim has been issued
Pages
- Create an account
- Your firm address
- Your firm contact details
- Your reference for this claim
- Choose a county court
- Choose claimant type
- Claimant Address
- Choose defendant type
- Defendant Address
- Defendant Service address
- Summarise the claim
- Type of claim
- Enter claim value
- Is it a personal injury claim?
- Is it a claim for housing disrepair?
- Calculating your fee
- Check the claim details
- Do you want to download a draft of the claim form for your client to sign?
- Statement of truth
- Choose how to pay
- Pay by Fee Account
- Pay by credit card
- Your claim has been issued
Pages
- Create an account
- Your firm address
- Your firm contact details
- Your reference for this claim
- Choose a county court
- Choose claimant type
- Claimant Address
- Choose defendant type
- Defendant Address
- Defendant Service address
- Summarise the claim
- Type of claim
- Enter claim value
- Is it a personal injury claim?
- Is it a claim for housing disrepair?
- Calculating your fee
- Check the claim details
- Do you want to download a draft of the claim form for your client to sign?
- Statement of truth
- Choose how to pay
- Pay by Fee Account
- Pay by credit card
- Your claim has been issued
{% endblock %}


A class to handle integral image.
More...
#include <Integral.hpp>
|
| template<class D > |
| static Image< double > | IntegralImage (const Image< D > &img) |
| | Returns the integral image based on img. More...
|
| |
| static double | WindowIntegralValue (const Image< double > &integral, const Vector< size_t > &window_end, const Vector< size_t > &window_size) |
| | Returns the integral intensity value of window, with posterior extremity in window_end in the integral image. More...
|
| |
| static double | WindowIntegralValue (const Image< double > &integral, const Vector< size_t > &window_end, const Vector< float > &window_size_mm) |
| | Returns the integral intensity value of window, with posterior extremity in window_end in the integral image. More...
|
| |
| static double | WindowVolume (const Vector< size_t > &window_size) |
| | Returns the volume of the window. More...
|
| |
| template<class D > |
| static double | WindowVolume (const Image< D > &img, const Vector< float > &window_size_mm) |
| | Returns the volume of the window according to img pixel dimensions in millimeters. More...
|
| |
| static Vector< size_t > | PixelWindow (const Vector< float > &pixel_size, const Vector< float > &window_size_mm) |
| | Returns a window with the same dimensions as the input described in number of pixels instead of in millimeters. More...
|
| |
| static double | WindowDensity (const Image< double > &integral, const Vector< size_t > &window_end, const Vector< size_t > &window_size) |
| | Returns the density value of window, with posterior extremity in window_end in the integral image. More...
|
| |
| static double | WindowDensity (const Image< double > &integral, const Vector< size_t > &window_end, const Vector< float > &window_size_mm) |
| | Returns the density value of window, with posterior extremity in window_end in the integral image. More...
|
| |
A class to handle integral image.
Definition at line 22 of file Integral.hpp.
template<class D >
| static Image< double > Bial::Integral::IntegralImage |
( |
const Image< D > & |
img | ) |
|
|
static |
Returns the integral image based on img.
- Date
- 2013/Aug/27
- Parameters
-
- Returns
- Integral image based on img.
- Warning
- none.
Returns a window with the same dimensions as the input described in number of pixels instead of in millimeters.
- Date
- 2013/Aug/29
- Parameters
-
| pixel_size | pixel dimensions. |
| window_size_mm | window dimensions in millimeters. |
- Returns
- A window with the same dimensions as the input described in number of pixels instead of in millimeters.
- Warning
- none.
Returns the density value of window, with posterior extremity in window_end in the integral image.
- Date
- 2013/Aug/29
- Parameters
-
| integral | An integral image. |
| window_end | Greater coordinates of the window. |
| window_size | Window dimensions in pixels. |
- Returns
- Density value of window, with posterior extremity in window_end in the integral image.
- Warning
- none.
Returns the density value of window, with posterior extremity in window_end in the integral image.
- Date
- 2013/Aug/29
- Parameters
-
| integral | An integral image. |
| window_end | Greater coordinates of the window. |
| window_size_mm | Window dimensions in millimeters. |
- Returns
- Density value of window, with posterior extremity in window_end in the integral image.
- Warning
- none.
| static double Bial::Integral::WindowIntegralValue |
( |
const Image< double > & |
integral, |
|
|
const Vector< size_t > & |
window_end, |
|
|
const Vector< size_t > & |
window_size |
|
) |
| |
|
static |
Returns the integral intensity value of window, with posterior extremity in window_end in the integral image.
- Date
- 2013/Aug/29
- Parameters
-
| integral | An integral image. |
| window_end | Greater coordinates of the window. |
| window_size | window dimensions in pixels. |
- Returns
- Integral intensity value of window, with posterior extremity in window_end, in the integral image.
- Warning
- none.
| static double Bial::Integral::WindowIntegralValue |
( |
const Image< double > & |
integral, |
|
|
const Vector< size_t > & |
window_end, |
|
|
const Vector< float > & |
window_size_mm |
|
) |
| |
|
static |
Returns the integral intensity value of window, with posterior extremity in window_end in the integral image.
- Date
- 2013/Aug/29
- Parameters
-
| integral | An integral image. |
| window_end | Greater coordinates of the window. |
| window_size_mm | window dimensions in millimeters. |
- Returns
- Integral intensity value of window, with posterior extremity in window_end, in the integral image.
- Warning
- none.
| static double Bial::Integral::WindowVolume |
( |
const Vector< size_t > & |
window_size | ) |
|
|
static |
Returns the volume of the window.
- Date
- 2013/Aug/29
- Parameters
-
| window_size | window dimensions in pixels. |
- Returns
- Volume of the window.
- Warning
- none.
template<class D >
| static double Bial::Integral::WindowVolume |
( |
const Image< D > & |
img, |
|
|
const Vector< float > & |
window_size_mm |
|
) |
| |
|
static |
Returns the volume of the window according to img pixel dimensions in millimeters.
- Date
- 2013/Aug/29
- Parameters
-
| img | An image. |
| window_size_mm | window dimensions in millimeters. |
- Returns
- Volume of the window according to img pixel dimensions in millimeters.
- Warning
- none.
The documentation for this class was generated from the following file:


1.Set Limit
You must set ilmit number choosen.
2.Special Number
You can set special number which may be selected more than once. If you write here, you must write "," as a sepalater between numbers.
You must set ilmit number choosen.
You can set special number which may be selected more than once. If you write here, you must write "," as a sepalater between numbers.


Sprites made with rinmarugames.comMusic from Danganronpa Series, Professor Layton and the Curious Village, and M2ULove and support from Sir Tarcy


SINUS CONGESTION AND PAIN DAYTIME- acetaminophen and phenylephrine hydrochloride tablet, film coated
Kroger Company
Disclaimer: Most OTC drugs are not reviewed and approved by FDA, however they may be marketed if they comply with applicable regulations and policies. FDA has not evaluated whether this product complies.
Active ingredients (in each caplet)
Acetaminophen 325 mg
Phenylephrine HCl 5 mg
Purpose
Pain reliever/fever reducer
Nasal decongestant
Uses
- •
- temporarily relieves these symptoms associated with hay fever or other upper respiratory allergies, and the common cold:
- •
- headache
- •
- sinus congestion and pressure
- •
- nasal congestion
- •
- minor aches and pains
- •
- helps decongest sinus openings and passages
- •
- promotes sinus drainage
- •
- helps clear nasal passages
- •
- temporarily reduces fever
Warnings
Liver warning: This product contains acetaminophen. Severe liver damage may occur if you take
- •
- more than 4,000 mg of acetaminophen in 24 hours
- •
- with other drugs containing acetaminophen
- •
- 3 or more alcoholic drinks every day while using this product
Allergy alert: Acetaminophen may cause severe skin reactions. Symptoms may include:
- •
- skin reddening
- •
- blisters
- •
- rash
If a skin reaction occurs, stop use and seek medical help right away.
Do not use
- •
- with any other drug containing acetaminophen (prescription or nonprescription). If you are not sure whether a drug contains acetaminophen, ask a doctor or pharmacist.
- •
- if you are now taking a prescription monoamine oxidase inhibitor (MAOI) (certain drugs for depression, psychiatric, or emotional conditions, or Parkinson’s disease), or for 2 weeks after stopping the MAOI drug. If you do not know if your prescription drug contains an MAOI, ask a doctor or pharmacist before taking this product.
- •
- if you have ever had an allergic reaction to this product or any of its ingredients
Ask a doctor before use if you have
- •
- liver disease
- •
- heart disease
- •
- high blood pressure
- •
- thyroid disease
- •
- diabetes
- •
- trouble urinating due to an enlarged prostate gland
Ask a doctor or pharmacist before use if you are
taking the blood thinning drug warfarin
When using this product
do not exceed recommended dosage
Stop use and ask a doctor if
- •
- nervousness, dizziness, or sleeplessness occur
- •
- pain or nasal congestion gets worse or lasts more than 7 days
- •
- fever gets worse or lasts more than 3 days
- •
- redness or swelling is present
- •
- new symptoms occur
These could be signs of a serious condition.
If pregnant or breast-feeding,
ask a health professional before use.
Keep out of reach of children.
Overdose warning: In case of overdose, get medical help or contact a Poison Control Center right away (1-800-222-1222). Quick medical attention is critical for adults as well as for children even if you do not notice any signs or symptoms.
Directions
- •
- do not take more than directed (see overdose warning)
adults and children 12 years and over |
- •
- take 2 caplets every 4 hours
- •
- swallow whole – do not crush, chew or dissolve
- •
- do not take more than 10 caplets in 24 hours
|
children under 12 years |
ask a doctor |
Inactive ingredients
acesulfame potassium, carnauba wax, crospovidone, D&C yellow #10 aluminum lake, FD&C blue #1 aluminum lake, FD&C red #40 aluminum lake, flavor, lecithin, microcrystalline cellulose, polyethylene glycol, polyvinyl alcohol, povidone, pregelatinized starch, stearic acid, talc, titanium dioxide
Questions or comments?
1-800-632-6900
Principal Display Panel
COMPARE TO the active ingredients of TYLENOL® SINUS CONGESTION & PAIN CAPLETS DAYTIME
See top panel
SEE NEW WARNINGS
DAYTIME NON-DROWSY
For Adults
Sinus Congestion & Pain
Pain Reliever / Fever Reducer
Nasal Decongestant
Headache, Acetaminophen
Sinus Pressure, Nasal Congestion
Phenylephrine HCl
COOL ICE®
Instant Cooling Sensation
actual size
24 CAPLETS


Political Ads on Facebook by Jeff Larson, Rob Weychert, Julia Angwin and Jennifer Valentino-DeVries December 4, 2017


{{ pipeline.locked.description || 'This pipeline is locked and does not allow modification' }}
Manual edits are not allowed on templated pipelines
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configuration is currently rendered using
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configuration cannot be rendered before the pipeline has executed at least once.
Could not render the pipeline configuration
because of an error: {{ templateError.data.message }} {{ templateError.data.error }}


There are unregistered email addresses in the approver list:
Before you can start the approval process all addresses must be confirmed by the addresses owners.
As soon as you start the verification an invitation email will be sent to unregistered users.
In this email the users will be requested to confirm by clicking on an enclosed link.
It is recommended that you additionally contact those users personally to encourage them to complete the confirmation step.
You will be able to start the approval process not before all email addresses has been validated successfully.


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MapReduce Overview
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India's Favourite Family Car
Thanks will call you soon
India's Favourite Family Car is here to rule your hearts with a well crafted design and an impressive presence. Step in to discover comfort and convenience and enjoy a host of advanced features that are simply a class apart.
Designed for the entire family's pleasure, the All New SANTRO comes with a modern design and a confident stance
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 You can now log in and use KEIKEN. Thanks for signing up!{% endblocktrans %}
{% endblock %}


module State:sig
..end
Tracks the current state of the world.
type
t = {
|
civilizations : |
|
disease : |
|
name : |
|
upgrades : |
|
tiles : |
|
elapsed_time : |
|
civcoords : |
|
news_message : |


Offriamo la migloire food in citta.
We would like to hear from you concerning our services and also any issue you would like us to handle so as to make you comfortable. Please be free to give us your feedback which will be greatly appreciated through our social media pages.


技术002KNCalico
技术002KNCalico¶
Requirements¶
Basic¶
Calico can run on any Kubernetes cluster which meets the following criteria.
The kubelet must be configured to use CNI network plugins (e.g –network-plugin=cni).
The kube-proxy must be started in iptables proxy mode. This is the default as of Kubernetes v1.2.0.
The kube-proxy must be started without the –masquerade-all flag, which conflicts with Calico policy.
The Kubernetes NetworkPolicy API requires at least Kubernetes version v1.3.0.
IP pool¶
calico网段默认是
in Kubernetes, all three of the following arguments must be equal to, or contain, the Calico IP pool CIDRs:
kube-apiserver: –pod-network-cidr kube-proxy: –cluster-cidr kube-controller-manager: –cluster-cidr


{{fontItem.defSubset}}
){{fontItem.defVariant}}
)The quick brown fox jumps over the lazy dog.
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Click on code to select all statements, then copy/paste it into your own CSS file.
Your generated archive for {{fontItem.family}} with charsets [{{downloadSubSetID}}]
and styles [{{variantDownloadQueryString}}]
includes the formats [{{fontFormats}}]
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Protest against Co Leitrim asylum housing plans stood down with immediate effect
A group that has been protesting against plans to house asylum seekers in an apartment complex in Co Leitrim have stood down their protest with immediate effect.
On Friday, the owners of the apartment complex in Ballinamore secured an injunction preventing protesters interfering with works to complete the structure.
Ballinamore Community Group have agreed to stand down their demonstration with immediate effect.
The group had been holding a silent 24-hour protest outside the apartment block since mid-October where there were plans to house 130 asylum seekers.
Without your support and the support of everyone who has helped in any way with this demonstration we would not have reached this juncture
In a statement, Ballinamore Community Group said they have received communication from Minister of State for Immigration David Stanton’s department regarding the plans.
“They have given a firm commitment to not do anything further in Ballinamore until further negotiations take place with our spokespersons.
“With this in mind we have agreed with the Minister to stand down our silent and peaceful demonstration at the Rock Quarter with immediate effect.
“Without your support and the support of everyone who has helped in any way with this demonstration we would not have reached this juncture.
“We remain steadfast in our position and will continue to negotiate on behalf of the community.”
Mr Hughes said the group are not against immigration, but the current direct provision system for asylum seekers is broken and does not meet their needs.
He said Ballinamore has a population of 900 people, and the planned number of asylum seekers it is proposed are housed there is not proportional to the size of the town.
The Department of Justice has also faced resistance to opening new direct provision centres, with arson attacks at hotels in Moville, Co Donegal, and Rooskey in Co Roscommon, earlier this year.
READ MORE
Eoin Ó Broin: Housing is 'single biggest failure' of coalition Government


Across: 7. Something Dan sketched in 8 down city (6) 8. Operated on our piles, which is far from safe (8) 9. Land reform lies! (4) 10. Coital lip-licking possibly providing concern for member (9) 11,20dn. Demonstrations perhaps of 8 down 1968 10 17 (5 6) 12. With values unchanged by direction East going, poor cities rebuilt (9) 15,16. 1968 thaw sparing purge possibly? (6 6) 18. They farm vegetables - an effort! (9) 19. Partied and ruined grade (5) 22. Imperial arm is Tsars' offering - they are quick to percieve a 17 (9) 24. Extremes of candour endow workers as a group (4) 25. Poles following government programs for health's sake (8) 26. Dissect parts, ignoring a second's orders (6) Down: 1. City does as directed (6) 2. Perhaps 18 European articles initially cheer girl (10) 3. Leaves out bins (5) 4. Silly, petty... poor model (9) 5.  Persecution by union's lawful leaders, along with dishonesty (8) 8. Shine at being removed from awful hospital (6) 13. Using dubious logic, chair ruled with a small cohort (10) 14. Rub men and overwhelm (9) 15. Cultivated in advance of normal education (8) 17. Panic as councillor meets with terror group (6) 20. see 11ac 21. Non-aligned movement's weak (5) 23. Chopper is for alliance (4)


Creating awareness on stomach cancer
by Staff ReporterA book titled Stomach cancer - From prevention to cure, to create awareness on stomach cancer, was released by Health and Family Welfare Minister C. Vijaya Baskar on Sunday.
S.M. Chandramohan, gasterointestinal surgeon and founder of EsoIndia, who authored the book, said the intention was to create awareness on early symptoms of stomach cancer and treatment options available. “One of the primary problems is that the patients are not aware of the symptoms and when they approach the doctor at a later stage, it is more complicated,” he said. Dr. Vijaya Baskar said the Department would soon sign an MoU with EsoIndia to take forward the awareness on stomach cancer.
Lauding the efforts of Dr. Chandramohan, Justice RMT. Teekaa Raman, judge, Madras High Court, received the first copy.
Actor Vivekh said there was an increased consensus among health professionals that a person’s well-being was mainly dependent on how good their digestive system is.


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1 : /* 2 : * This is the header file for the MD5 message-digest algorithm. 3 : * The algorithm is due to Ron Rivest. This code was 4 : * written by Colin Plumb in 1993, no copyright is claimed. 5 : * This code is in the public domain; do with it what you wish. 6 : * 7 : * Equivalent code is available from RSA Data Security, Inc. 8 : * This code has been tested against that, and is equivalent, 9 : * except that you don't need to include two pages of legalese 10 : * with every copy. 11 : * 12 : * To compute the message digest of a chunk of bytes, declare an 13 : * MD5Context structure, pass it to MD5Init, call MD5Update as 14 : * needed on buffers full of bytes, and then call MD5Final, which 15 : * will fill a supplied 16-byte array with the digest. 16 : */ 17 : 18 : #ifndef MD5_H 19 : #define MD5_H 20 : 21 : #include "files.h" 22 : 23 : struct MD5Context 24 : { 25 2 : MD5Context() { Init(); } 26 : 27 : void Init(); 28 : void Update(const BYTE *buf, unsigned len); 29 : void Update(FileReader *file, unsigned len); 30 : void Final(BYTE digest[16]); 31 : 32 : private: 33 : DWORD buf[4]; 34 : DWORD bytes[2]; 35 : DWORD in[16]; 36 : 37 : }; 38 : 39 : void MD5Transform(DWORD buf[4], DWORD const in[16]); 40 : 41 : #endif /* !MD5_H */


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Highlights
Horse Racing
Learn the art of Mongolian horse racing with your fellow travelers.
Yurt Camping
Camp on the Mongolian plains in your own private yurt with ensuite bath.
Eagle Experience
Experience the thrill of eagle handling with your guide and fellow travelers.
Archery Course
Learn basic and advanced archery techniques with your fellow travelers.
Lodging
Yurt
Get some rest in your private yurt with ensuite bath or socialize in the campground bar. This breezy yurt will keep you cool during the hot Mongolian days.
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Private Jet
The Mongolia Adventure Package includes a flight on a private jet to nearest airport. -
Premium Bedding
Memory foam mattress with goose-down bedding provided in every yurt. -
Private Ensuite Bath
Each yurt includes a private, ensuite bath. -
Gourmet Meals
The Mongolia Adventure Package includes three gourmet meals per day for the duration of your expedition. -
Satellite Internet
Reliable, high-speed, satellite Internet included in your stay. -
Campground Bar
Campground bar open nightly at 5 pm. Live entertainment on Fridays and Saturdays.
Guide
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Lisa Yun has fifteen years of experience leading tours in Mongolia. She is an expert on Mongolian culture and traditions. Lisa is available to answer any of your questions about our Mongolia Adventure Package. You may contact her via the form below.
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Greetings, readers! I’m Shafiya Heena, from Hyderabad, India, who now finds herself immersed in the vibrant city of Toronto, Canada. After spending six fruitful years as a DevOps Engineer, I recently embarked on a new professional journey with Creative Commons, a nonprofit organization. Today, I want to share my experiences and thoughts on the stark differences in culture between these two organizations and shed light on my recent encounter with an event called InTown Week (ITW).
Before joining Creative Commons, I had limited exposure to open source initiatives and nonprofit organizations. However, upon stepping foot into Creative Commons, I found myself captivated by its unique culture. The emphasis on collaboration, transparency, and fostering a sense of responsibility among staff members left me awestruck. The organization's commitment to open source and its associated ethos ignited a newfound passion within me.
Prior to ITW, I had heard positive whispers of this event, but had little knowledge about its significance. Little did I know that this week-long gathering would prove to be an enlightening and transformative experience. Over the course of five days, I delved deep into self-discovery, learning more about my team members, and gaining profound insights into Creative Commons as an organization.
One aspect that particularly struck me during ITW was the knowledgeable, sense of equality among staff members. Everyone was encouraged to share their opinions, even if they contradicted the prevailing decisions. This environment fostered a culture of inclusivity and collective growth. Additionally, I had the opportunity to take an insightful discovery test that provided valuable insights into my personality traits and how I could enhance my contributions within the team.
During all the conversations and activities, I found myself occasionally getting lost in the whirlwind of information. Thankfully, I was fortunate to have a dedicated mentor who skillfully guided me back on track, ensuring that I comprehended the nuances of the happenings around me. Through this mentorship, I discovered how to boost my energy levels and become an even better fit within the team.
While the experience of ITW was enriching, it was not without its challenges. Due to visa issues, I was not able to attend in person. Engaging in extended virtual calls throughout the week was demanding, but the active participation from every individual in all activities made it all worthwhile. One particularly heartwarming moment was bidding farewell, as everyone stood in a row to say goodbye to me personally on the screen. This gesture made me feel truly present, transcending the virtual realm, and I express my heartfelt gratitude to my mentor for facilitating such connections.
A notable contrast I observed during my time at Creative Commons was the vivaciousness and approachability of the CEO. In stark contrast to my previous organization, where CEO communication was primarily limited to formal emails announcing changes or decisions, I was pleasantly surprised by the CEO's warmth and genuine interest in engaging with employees in a personable manner. This refreshing leadership style evoked a sense of enthusiasm and bolstered my commitment to the organization's goals.
To encapsulate my thoughts on the open source culture at Creative Commons in a single word, I would choose "likable." My mentor, in particular, played a crucial role in establishing transparency by designing a comprehensive three-month onboarding document, which laid out expectations and goals. Here, although the infrastructure may be smaller, the workflow is streamlined, and there is an absence of restrictive IT teams that hinder access to websites or prioritize hardware security over staff responsibility. Instead, Creative Commons embraces a culture where employees take ownership of their hardware, fostering an environment of trust and empowerment.


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[Note that followups are directed to rec.arts.movies.current-films and rec.arts.movies.startrek.current only, not to rec.arts.sf.movies. -Moderator]
STAR TREK: FIRST CONTACT [Spoilers] A film review by Chuck Dowling Copyright 1996 Chuck Dowling
Star Trek:First Contact (1996) ** out of ***** - C:Patrick Stewart, Jonathan Frakes, Brent Spiner, LeVar Burton, Michael Dorn, Gates McFadden, Marina Sirtis, James Cromwell, Alfre Woodard, Dwight Schultz, Robert Picardo.
***This review does contain spoilers***
I was completely unprepared for how bad this movie is. After seeing about a thousand commercials for the newest Star Trek adventure, I was sold. Finally, Starfleet battles their dreaded enemy from the series, the Borg. Finally, an action packed Star Trek movie which many had called "Die Hard on the Enterprise". I was one of the first in line. Once it began, I couldn't keep from wanting to leave.
If you are a casual Star Trek fan, you'll be lost from the first minute. If you are a serious Star Trek fan, I think you'll feel cheated. First of all, nothing is really explained in the film. As soon as the film starts, the Borg attack. You aren't even given a second to settle into the film. And after that it's just "kill the Borg". How are they doing it? Don't ask. Just watch. Will it make sense? No. Just watch.
When we first see Geordi (Burton), he is no longer wearing his visor. He apparently no longer needs it. Well when did that happen? In the last film, the Enterprise was completely destroyed. So are we introduced to the new ship as we were in the original Star Trek film? Nope, not even for a second. When the Enterprise was destroyed in "The Search for Spock" and in "Generations", you cared because the characters cared. Here, when the ship is facing destruction, who cares? Blow it up. Starfleet has changed their uniforms AGAIN, now they look like jumpsuits from the 1970s. Would you feel safe being protected by an organization in charge of defending the universe that can't even make up their minds about how to dress?
Also, none of the characters in the film even seem familiar anymore.  The rest of the crew is given little or nothing to do. Captain Picard (Stewart) turns into a madman upon encountering the Borg, because of events which took place during the series' third and fourth seasons. Forget the fact that the Enterprise crew encountered the Borg two more times after that in the series. They act as if those seemingly important events never happened. I don't like that. It's like I'm being lied to.
The Borg as an enemy seemed vicious in the series, but now they seem pretty ridiculous. As smart and advanced as these things are, they still only attack when provoked. This is how they always lose, because the crew just stops fighting them and figures some other way to destroy them. The Borg will just walk right by members of the crew as long as they aren't a direct threat. Meanwhile those crew members keep on walking and eventually devise a way to destroy them. This is a movie folks! You writers could have raised the stakes just a little!
A Borg leader is introduced here, something which also seems to be a contridiction from the series. Picard confronts her and tells her that he destroyed her years ago. She admits that is true, but that humans need to look beyond thinking 3 dimensionally, perhaps meaning that the Borg cannot be killed by conventional means. But then, later in the film, she and the other Borg are killed by some sort of gas that was contained in the engine room, which had become their hive. (That poses the Wizard of Oz question: If water killed you, why would you keep a bucket of it near you?) So what was the point of all that discussion?
Also, in the TV series, the writers made a point to explain EVERYTHING, even if it was just a technobabble explanation. Never once can I recall did I question the logic of any episode of "The Next Generation". Here, they don't bother to explain anything. The Borg have developed time travel capabilities. They send only one ship to Earth (just as they did before, way to learn from previous mistakes borgies) and then send a smaller ship back in time upon reaching Earth. Well, the Enterprise is prepared for this because they see it happening. Wouldn't it make sense to go back in time BEFORE reaching Earth so no one knows you're doing it?
There's a pointless time travel subplot which doesn't make any sense either. Throughout the history of Star Trek, everyone always made a big deal about time travel, about how not to interfere with events in the past. Well not only does the crew interfere with MAJOR events, they actually participate in them too! Ridiculous.
For a series which prided itself in continuity and details, it's shocking to see that this film throws all of that out of the window. There's a Star Trek novel called "Vendetta" which deals with the Borg, and that would have been far superior to this. Maybe when making the next film, the filmmakers can pretend that this one didn't happen. Why not? They got plenty of practice ignoring their own history in this one.
-- Chuck Dowling Reply to<EMAIL_ADDRESS>Visit Chuck's Movie Reviews at http://users.southeast.net/~chuckd21/ Over 1,600 movies rated and/or reviewed! Movie news, box office
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Do you sometimes feel …

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Your current life strategy is not delivering the goods?
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Through Liminality, I offer scientific, clear, and measurable insight into your unknown strategies and help you remodel stumbling blocks into springboards. This is vastly different from personality analysis.
It gets to the root of the problem, not the symptoms!
The duration of the course is 21 days. The total cost is R20 000 (non-refundable) and can be partially claimed back from medical aid. If you are not a suitable candidate, you will be liable for a R1 000 diagnostic session. Because of the personal nature of the treatment, clients need to live in the broader Cape Town area.
“I entrust my entire family in Gideon’s therapeutic care. He is a breath of fresh air in the stale world of traditional therapy. No wonder that after only 6 months in the UK he was already interviewed by BBC News. Brilliant!”
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fin project stop- Open a shell into a specific container:
fin bash container_name- Display logs of a specific container:
fin logs container_name- Display logs of a specific container and follow the log:
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The Hill We Climb
The Hill We Climb
A poem written by Amanda Gordon and delivered at the inaguration of Joe Biden.
JK Rowling's Commencement Speech
JK Rowling's Commencement Speech
The famous author spoke about failure and imagination to the graduating class of 2008.
Barak Obama's Victory Speech
Barak Obama's Victory Speech
This speech delivered to the world marked a moment of hope and promise - "Yes We Can!"
Why We have Too Few Female Leaders
Why We have Too Few Female Leaders
Facebook COO looks at why a smaller percentage of women than men reach the top of their professions.
The Truth is More Heroic than the Hype
The Truth is More Heroic than the Hype
Soldier Jessica Lynch reminds the House Oversight Committee and America the truth about war.


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Yong Wang
Professor
Institute of Applied Methematics
Academy of Methematics and Systems Science(AMSS)
Chinese Academy of Sciences(CAS)
No. 55 Zhongguacun East Road, Beijing, China
Email<EMAIL_ADDRESS>Office: Siyuan Building, Room 606
Phone: +86-10-82541359 (O), +86-13693300386 (C)
BIOGRAPHY
I am a
Professor in the Institute of Applied Mathematics
at Academy of Mathematics and Systems Science at at Chinese Academy of Sciences (CAS), where I am also affiliated as a Professor
in the National Center for Mathematics and Interdisciplinary Sciences (NCMIS) at Chinese Academy of Sciences,
in the Center for Excellence in Animal Evolution and Genetics (CEAEG) at Chinese Academy of Sciences,
in the School of Mathematics, University of Chinese Academy of Sciences at Chinese Academy of Sciences,
and in the Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences at Chinese Academy of Sciences.
I graduated from Inner Mongolia University with B.S. in Mathematics and Physics (1995-1999). I obtained my
M.S. degree from the Dalian University of Science and Technology in Operations Research and Control Theory (1999-2002). I received my Ph.D. in Operations Research and Control Theory from Academy of Mathematics and Systems Science at at Chinese Academy of Sciences (CAS) (2002-2005). My Ph.D thesis was specializing in
optimization model and algorithm for protein structure prediction and classification with Prof. Xiang-Sun Zhang. I carried out postdoctoral research in E-government strategy planning with Prof. Chun-Zheng Wang at the State Information Center in China and later in bioinformatics with Prof. Luonan Chen at the Department of Electronics Information and Communications, Osaka Sangyo University in Japan (2005-2007).
After joining Academy of Mathematics and Systems Science, I visited the Bioinformatics Program in Boston University as a research associate (2007-2008), the Computational Biology Research Center (CBRC) of National Institute of Advanced Industrial Science and Technology (AIST) in Japan as a research scientist (2010-2011), Department of Statistics, Bio-X program, and The Center for Personal Dynamic Regulome in Center of Excellence in Genomic Science (CEGS) in Stanford University as research associate (2013-2016).
.
Here is my Google Scholar profile.
SELECTED PAPERS
- Reusability report: Compressing regulatory networks to vectors for interpreting gene expression and genetic variants. Wanwen Zeng, Jingxue Xin, Rui Jiang, Yong Wang. Nature Machine Intelligence. 1-5 (2021).
- hReg-CNCC reconstructs a regulatory network in human cranial neural crest cells and annotates variants in a developmental context. Zhanying Feng, Zhana Duren, Ziyi Xiong, Sijia Wang, Fan Liu, Wing Hung Wong, Yong Wang. Communications Biology. vol. 4: 1-6 (2021).
- MIMIC: an optimization method to identify cell type-specific marker panel for cell sorting. Meng Zou, Zhana Duren, Qiuyue Yuan, Henry Li, Andrew Paul Hutchins, Wing Hung Wong, Yong Wang. bbab235. (2021).
- Chromatin accessibility landscape and regulatory network of high-altitude hypoxia adaptation. Nature Communications. vol. 11: 1-20 (2020).
- ERG orchestrates chromatin interactions to drive prostate cell fate reprogramming. The Journal of Clinical Investigation. vol. 130(11): 5924-5941 (2020).
- Time course regulatory analysis based on paired expression and chromatin accessibility data. Genome Research. vol. 30: 622-634 (2020).
- 3Scover: Identifying safeguard TF from cell type-TF specificity network by an extended minimum set cover model. iScience. vol. 23(6): 101227. (2020).
- Integrating distal and proximal information to predict gene expression via a densely con-nected convolutional neural network. Bioinformatics. vol. 36, Issue 2, P496-503 (2020).
- Associating lncRNAs with small molecules via bilevel optimization reveals cancer-related lncRNAs. PLoS Computational Biology. vol. 15 (12), e1007540 (2019).
- Lipid-gene regulatory network reveals coregulations of triacylglycerol with phosphatidylinositol/lysophosphatidylinositol and with hexosyl-ceramide. Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids. vol. 1864, Issue 2, Pages 168-180 (2019).
- scTIM: Seeking Cell-Type-Indicative Marker from single cell RNA-seq data by consensus optimization. Bioinformatics. vol. 36, Issue 8, P2474ĘC2485, (2019).
- DC3 is a method for deconvolution and coupled clustering from bulk and single-cell genomics data. Nature communications. vol. 10 (1), 1-11 (2019).
- Hierarchical graphical model reveals HFR1 bridging circadian rhythm and flower development in Arabidopsis thaliana. npj Systems Biology and Applications. vol. 5(1), 1-11 (2019).
- TFAP2C- and p63-Dependent Networks Sequentially Rearrange Chromatin Landscapes to Drive Human Epidermal Lineage Commitment. Cell Stem Cell. Vol. 24, Issue 2, P271-284.E8, (2019).
- Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations. Proceedings of the National Academy of Sciences. 115 (30) 7723-7728 (2018).
- Modeling gene regulation from paired expression and chromatin accessibility data. Proceedings of the National Academy of Sciences. vol. 114 no. 25 E4914-E4923 (2017).
- A systematic method to identify modulation of transcriptional regulation via chromatin activity reveals regulatory network during mESC differentiation. Scientific Report 6, 22656 (2016).
- NCC-AUC: an AUC optimization method to identify multi-biomarker panel for cancer prognosis from genomic and clinical data. Bioinformatics 31 (20), 3330-3338 (2015).
- Computational probing protein-protein interactions targeting small molecules. Bioinformatics. 32 (2), 226-234 (2015).
- A novel mixed integer programming for multi-biomarker panel identification by distinguishing malignant from benign colorectal tumors. Methods. 83: 3-17 (2015).
-
Drug repositioning by kernel-based integration of molecular structure, molecular activity, and phenotype data.
PLoS One 8 (11), e78518 (2013).
- Network predicting drug's anatomical therapeutic chemical code. Bioinformatics 29 (10), 1317-1324 (2013).
- ellipsoidFN: a tool for identifying a heterogeneous set of cancer biomarkers based on gene expressions.
Nucleic Acids Research 41 (4), e53-e53 (2012).
- Revealing metabolite biomarkers for acupuncture treatment by linear programming based feature selection. BMC Systems Biology. 6 (1), S15 (2012)
- A combinatorial model and algorithm for globally searching community structure in complex networks.
Journal of Combinatorial Optimization. 23 (4), 425-442 (2012).
- A linear programming framework for inferring gene regulatory networks by integrating heterogeneous data.
Handbook of Research on Computational Methodologies in Gene Regulatory Networks 450-475 (2010).
- Protein evolution in yeast transcription factor subnetworks. Nucleic Acids Res. 38: 5959-5969 (2010).
-
Predicting eukaryotic transcriptional cooperativity by Bayesian network integration of genome-wide data.
Nucleic Acids Res. 37: 5943-5958 (2009).
- Evaluating protein similarity from coarse structures. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 6 (4), 583-593
(2009).
- A network biology study on circadian rhythm by integrating various omics data. OMICS A Journal of Integrative Biology. 13 (4), 313-324 (2009).
- Condition specific subnetwork identification using an optimization model. Lecture Notes in Operations Research. 9, 333-340 (2008).
- Analysis on multi-domain cooperation for predicting protein-protein interactions. BMC bioinformatics. 8 (1), 391 (2008).
- Inferring gene regulatory networks from multiple microarray datasets. Bioinformatics. 22 (19), 2413-2420 (2006).
- Exploring protein's optimal HP configurations by self-organizing mapping. Journal of bioinformatics and computational biology. 3 (02), 385-400 (2005).
- A new trust region method for nonlinear equations. Mathematical Methods of Operations Research. 58 (2), 283-298 (2003).
|


On 11 and 12 January 2020, WHO received further detailed information from the National Health Commission about the outbreak.
WHO is reassured of the quality of the ongoing investigations and the response measures implemented in Wuhan, and the commitment to share information regularly.
The evidence is highly suggestive that the outbreak is associated with exposures in one seafood market in Wuhan. The market was closed on 1 January 2020. At this stage, there is no infection among healthcare workers, and no clear evidence of human to human transmission. The Chinese authorities continue their work of intensive surveillance and follow up measures, as well as further epidemiological investigations.
Among the 41 confirmed cases, there has been one death. This death occurred in a patient with serious underlying medical conditions.
China shared the genetic sequence of the novel coronavirus on 12 January, which will be of great importance for other countries to use in developing specific diagnostic kits.
The cluster was initially reported on 31 December 2019, when the WHO China Country Office was informed. The Chinese authorities identified a new type of coronavirus (novel coronavirus, nCoV), which was isolated on 7 January 2020. Laboratory testing was conducted on all suspected cases identified through active case finding and retrospective review. Other respiratory pathogens such as influenza, avian influenza, adenovirus, Severe Acute Respiratory Syndrome coronavirus (SARS-CoV), Middle East Respiratory Syndrome coronavirus (MERS-CoV) were ruled out as the cause.
According to information conveyed to WHO by Chinese authorities on 11 and 12 January, 41 cases with novel coronavirus infection have been preliminarily diagnosed in Wuhan City. Of the 41 cases reported, seven are severely ill. This is when the one death, mentioned above, was reported, in a patient with other underlying health conditions. Six patients have been discharged from hospital. Symptom onset of the 41 confirmed nCoV cases ranges from 8 December 2019 to 2 January 2020. No additional cases have been detected since 3 January 2020.
The clinical signs and symptoms reported are mainly fever, with a few cases having difficulty in breathing, and chest radiographs showing invasive pneumonic infiltrates in both lungs. National authorities report that patients have been isolated and are receiving treatment in Wuhan medical institutions.
According to the preliminary epidemiological investigation, most cases worked at or were handlers and frequent visitors to the Huanan Seafood Wholesale Market. The government reports that there is no clear evidence that the virus passes easily from person to person.
Currently, no case with infection of this novel coronavirus has been reported elsewhere other than Wuhan.
China's National Health Commission has deployed a group of experts to Wuhan City to support the local response.
National authorities have reported the following response measures:
WHO is closely monitoring the situation and is in regular contact with national authorities in China to provide support required. Technical guidance on novel coronavirus has been developed and will continue to be updated as additional information is available.
To date, investigations are still under way to assess the full extent of the outbreak.
Wuhan city is a major domestic and international transport hub. To date, there have been no reported cases outside of Wuhan City.
More comprehensive information and ongoing investigations are also required to better understand the epidemiology, clinical picture, source, modes of transmission, and extent of infection; as well as the countermeasures implemented.
Based on information provided by national authorities, WHO’s recommendations on public health measures and surveillance for novel coronaviruses apply.
WHO does not recommend any specific health measures for travellers. In case of symptoms suggestive of respiratory illness either during or after travel, travellers are encouraged to seek medical attention and share travel history with their healthcare provider. Travel guidance has been updated.
WHO advises against the application of any travel or trade restrictions on China based on the information currently available on this event.
For more information:


Introduction
After analyzing who the speakers are, giving an overview of names, jobs and other profile information, this chapter deeper examines what the different speakers from the academic world think about electronic mass surveillance. Coherently with the previous research (see protocol in chapter 02) also the debate among academics is about how much and why mass surveillance practicies are wrong, so it is interesting to analyze the different aspects of the issue. The chapter presents two different visualizations. The first one is an alluvial diagram focusing on the relations between the field of studies/point of view of the speakers, their opinions about the topics and the possible solution they gives to make the situation turning better. The second visualization deeper analyzes the keywords used from the speakers with the same opinion and shows a comparison between them. Summarizing the research query used in this step is "how the debate develops in the academic world and which are the main actors".
How to read the visualization
On the first row there are the different points of view from which the authors speak, in the one below are the main opinions and in the last one the poposals. The lenght of the bars represents how many papers have that tag, the total on every row is 55 (100% of the selected ones), so each document can only have one label. The reading order goes from the top to the deep end and it is possible to select a single cluster by passing the mouse over the curves, for a better reading.
ABOUT THE CLUSTERS MEANING
-Pro surveillance (pro surv.): those who are totally in favour of electronic surveillance.
-Surveillance is necessary: in favour of surveillance, that is necessary to mantain safety, but present practicies and laws have something wrong.
-Surveillance practicies are illegal: those who are not totally against surveillance but they assert surveilance practicies are not legal.
-Against surveillance: electronic surveillance is totally wrong, it is not only illegal but also a challenge to human rights and democracy. It is wrong under any aspects.
-No position: those who do not express an opinion about the issue.
How it has been done
The specific dataset was built by reading the 55 selected papers and giving specific tags. The documents were then clustered into more generic categories and finally Google Refine was used to adjust the dataset. The alluvial diagram was created using app.raw.densitydesign.org. The .svg file was downloaded and modified with Adobe Illustrator. The final .svg file of the visualization was then inserted in a .html file and interactions were added.
keywords and actors
How to read the visualization
Aim of the visualization is to show how the speakers of the four main "opinion" clusters deal with the topic and which actors emerge. The cluster "in favour of surveillance" has been omitted because only one paper on 55 is actually in favour of mass surveillance, and it is not comparable with the other categories. The 10 most frequently keywords are compared and splitted in two groups: generic words about mass surveillance, not specific of the academic field, and words used to give a specific orientation to the paper.
How it has been done
The dataset was building after collecting the keywords of each paper. In almost each document there are keywords given by the author, in few case we had to find the keywords by reading the whole paper. At least four keywords per paper were collected, adjusted and counted. The histogram only represent the ten most frenquent words for each cluster.
Findings
Most of the authors discuss the topic from a legislative/constitutional point of view, generally speaking a part of them considers mass surveillance as necessary for the national security but recognises there is something wrong , another part thinks mass surveillance is illegal expecially speaking about United States Constitution. In the academic field the debate is also about "how to solve it". Most of the speakers finish the paper with a proposal to solve the actual situation.The main debate in this case is among those who assert laws and rules have to be changed, those who think it is not a legislative matter but surveillance practicies have to be improved and shaped, and those who shift the debate on how to evade the "big eye", so the only possibility is to try not to be monitored. The last smaller group represents speakers asserting transparency will be the solution, people should know what the governments are planning and that will be enough. The clusters "changing or making new laws" and "reviewing surveillance practicies" are both about changing the institutions and what is behind the mass surveillance (government/secret services), while who asks for more transparency and the cluster "evading intrusions" sustain internet users should be able to choose among privacy and security. In brief the debate consists of opinions and ways of thinking distinguished by small hints. If bigger clusters should be made in the first visualization, on the "how to get off the actual situation" row the first would be "operating on institutions and authorities" and the second one "giving the necessary knowledge to users". Speaking about keywords and actors, the second visualization highlights that some keywords are common to all the speakers while some others are used to give the paper a specific orientation, such as constitutional amendments or expressions like "expectation", "distrust" or "resistance". Following this step the reaserch moved to the analysis of the references of the papers, to find others actors and links between the speakers. For this phase see chapter three.


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EXCLUSIVE: Goliath Girls #5 Brings Eunice to the Kaijusphjere
by L.D. NolanComiXology Originals has given CBR an exclusive preview for Sam Humphries and Alti Firmansyah's Goliath Girls #5, which teases Eunice entering the Kaijusphjere.
You can see CBR's exclusive preview of the issue below along with a plot summary for it.
Goliath Girls #5
- Written by Sam Humphries
- Illustrated by Alti Firmansyah
- Colored by Brittany Peer
- Monsters will rise, cities will fall...but best friends are forever! Zelda, Eunice, and Juliet are three best friends: orphans of the Kaiju Generation, and adoptive mothers to their own baby Goliath! For years they've searched for the elusive King of All Goliaths, the one fabled to bring the Forever War to an end. But they're not the only ones on his trail. From Sam Humphries (Harley Quinn, Green Lanterns) and Alti Firmansyah (X-Men '92, Star Lord and Kitty Pryde), comes a story about hope during wartime, and the power of sisterhood. The fate of the world lies in the hands of three teenaged girls and their pet kaiju, as they fight to save us from the goliaths, and save the goliaths from us.
Goliath Girls #5 releases November 27 from comiXology Originals.
https://schema.orgBreadcrumbListListItem1Homehttps://www.cbr.com/ListItem2Comicshttps://www.cbr.com/category/comics/ListItem3Comic Newshttps://www.cbr.com/category/comics/news/ListItem4EXCLUSIVE: Goliath Girls #5 Brings Eunice to the Kaijusphjerehttps://www.cbr.com/goliath-girls-5-preview/


See: Description
| Interface | Description |
|---|---|
| Position |
A position in a security.
|
| Product |
The product details of a financial instrument.
|
| ProductTrade |
A trade that is directly based on a product.
|
| ResolvableTrade<T extends ResolvedTrade> |
A trade that can to be resolved using reference data.
|
| ResolvedProduct |
A product that has been resolved for pricing.
|
| ResolvedTrade |
A trade that has been resolved for pricing.
|
| SecuritizedProduct |
The product details of a financial instrument that is traded as a security.
|
| SecuritizedProductTrade |
A trade that is directly based on a securitized product.
|
| Security |
A security that can be traded.
|
| SecurityQuantity |
A quantity of a security.
|
| Trade |
A trade with additional structured information.
|
| TradeConvention |
A market convention for trades.
|
| TradeTemplate |
A template used to create a trade.
|
| Class | Description |
|---|---|
| GenericSecurity |
A generic security, defined in terms of the value of each tick.
|
| GenericSecurity.Meta |
The meta-bean for
GenericSecurity . |
| GenericSecurityPosition |
A position in a security, where the security is embedded ready for mark-to-market pricing.
|
| GenericSecurityPosition.Builder |
The bean-builder for
GenericSecurityPosition . |
| GenericSecurityPosition.Meta |
The meta-bean for
GenericSecurityPosition . |
| GenericSecurityTrade |
A trade representing the purchase or sale of a security,
where the security is embedded ready for mark-to-market pricing.
|
| GenericSecurityTrade.Builder |
The bean-builder for
GenericSecurityTrade . |
| GenericSecurityTrade.Meta |
The meta-bean for
GenericSecurityTrade . |
| PositionAttributeType<T> |
The type that provides meaning to a position attribute.
|
| PositionInfo |
Additional information about a position.
|
| PositionInfo.Meta |
The meta-bean for
PositionInfo . |
| PositionInfoBuilder |
Builder to create
PositionInfo . |
| SecurityAttributeType<T> |
The type that provides meaning to a security attribute.
|
| SecurityId |
An identifier for a security.
|
| SecurityInfo |
Information about a security.
|
| SecurityInfo.Meta |
The meta-bean for
SecurityInfo . |
| SecurityInfoBuilder |
Builder to create
SecurityInfo . |
| SecurityPosition |
A position in a security, where the security is referenced by identifier.
|
| SecurityPosition.Builder |
The bean-builder for
SecurityPosition . |
| SecurityPosition.Meta |
The meta-bean for
SecurityPosition . |
| SecurityPriceInfo |
Defines the meaning of the security price.
|
| SecurityPriceInfo.Meta |
The meta-bean for
SecurityPriceInfo . |
| SecurityTrade |
A trade representing the purchase or sale of a security,
where the security is referenced by identifier.
|
| SecurityTrade.Builder |
The bean-builder for
SecurityTrade . |
| SecurityTrade.Meta |
The meta-bean for
SecurityTrade . |
| TradeAttributeType<T> |
The type that provides meaning to a trade attribute.
|
| TradeInfo |
Additional information about a trade.
|
| TradeInfo.Meta |
The meta-bean for
TradeInfo . |
| TradeInfoBuilder |
Builder to create
TradeInfo . |
The trade model has three basic concepts, trades, securities and products.
A Trade
is the basic element of finance,
a transaction between two organizations, known as counterparties.
Most trades represented in the system will be contracts that have been agreed on a date in the past.
The trade model also allows trades with a date in the future, or without any date.
A Security
is a standard contract that is traded,
such as an equity share or futures contract. Securities are typically created once and shared
using an identifier, represented by a SecurityId
.
They are often referred to as reference data.
Securities may also be stored in a Position
instead
of in a Trade
.
A Product
is the financial details of the trade or security.
A product typically contains enough information to be priced, such as the dates, holidays, indices,
currencies and amounts. There is an implementation of Product
for each distinct type
of financial instrument.
Trades are typically classified as Over-The-Counter (OTC) and listed.
An OTC trade directly embeds the product it refers to.
As such, OTC trades implement ProductTrade
.
For example, consider an OTC instrument such as an interest rate swap.
The object model consists of a SwapTrade
that directly contains a Swap
,
where SwapTrade
implements ProductTrade
.
The key to understanding the model is appreciating the separation of products from trades. In many cases, it is possible to price the product without knowing any trade details. This allows a product to be an underlying of another product, such as a swap within a swaption.
A listed trade can be defined in two ways.
The first approach is to use SecurityTrade
.
A SecurityTrade
stores just the security identifier, quantity and trade price.
When the trade needs to be priced, the identifier can be resolved to a Security
using
ReferenceData
.
The reference data could be backed by an in-memory store or a database.
The second approach is to use a more specific trade type, such as
BondFutureTrade
.
These types include the product details directly so that no reference data is needed.
As such, this approach avoids the need to use the Strata Security
classes.
For example, consider a bond future.
In the first approach, the application would create a SecurityTrade
using the identifier of the future.
The reference data would be populated, mapping the identifier to an instance of BondFutureSecurity
and additional identifiers for each of the underlying FixedCouponBondSecurity
instances.
In the second approach, the trade would be defined using BondFutureTrade
. In this case,
the trade directly holds the product model of the BondFuture
and each underlying FixedCouponBond
.
There is thus no need to populate the reference data with securities.
The key to understanding the model is appreciating the separation of products from trades and securities. It is often possible to price either against the market or against a model. Details for pricing against the market are held in the security. Details for pricing against the model are held in the product.
Copyright 2009-Present by OpenGamma Inc. and individual contributors
Apache v2 licensed
Additional documentation can be found at opengamma.github.io/StrataDocs.


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