statsmodels.regression.linear_model.RegressionResults¶
-
class
statsmodels.regression.linear_model.
RegressionResults
(model, params, normalized_cov_params=None, scale=1.0, cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs)[source]¶ This class summarizes the fit of a linear regression model.
It handles the output of contrasts, estimates of covariance, etc.
Returns: Attributes
aic
Akaike’s information criteria. For a model with a constant . For a model without a constant .
bic
Bayes’ information criteria. For a model with a constant . For a model without a constant
bse
The standard errors of the parameter estimates.
pinv_wexog
See specific model class docstring
centered_tss
The total (weighted) sum of squares centered about the mean.
cov_HC0
Heteroscedasticity robust covariance matrix. See HC0_se below.
cov_HC1
Heteroscedasticity robust covariance matrix. See HC1_se below.
cov_HC2
Heteroscedasticity robust covariance matrix. See HC2_se below.
cov_HC3
Heteroscedasticity robust covariance matrix. See HC3_se below.
cov_type
Parameter covariance estimator used for standard errors and t-stats
df_model
Model degress of freedom. The number of regressors p. Does not include the constant if one is present
df_resid
Residual degrees of freedom. n - p - 1, if a constant is present. n - p if a constant is not included.
ess
Explained sum of squares. If a constant is present, the centered total sum of squares minus the sum of squared residuals. If there is no constant, the uncentered total sum of squares is used.
fvalue
F-statistic of the fully specified model. Calculated as the mean squared error of the model divided by the mean squared error of the residuals.
f_pvalue
p-value of the F-statistic
fittedvalues
The predicted the values for the original (unwhitened) design.
het_scale
adjusted squared residuals for heteroscedasticity robust standard errors. Is only available after HC#_se or cov_HC# is called. See HC#_se for more information.
history
Estimation history for iterative estimators
HC0_se
White’s (1980) heteroskedasticity robust standard errors. Defined as sqrt(diag(X.T X)^(-1)X.T diag(e_i^(2)) X(X.T X)^(-1) where e_i = resid[i] HC0_se is a cached property. When HC0_se or cov_HC0 is called the RegressionResults instance will then have another attribute het_scale, which is in this case is just resid**2.
HC1_se
MacKinnon and White’s (1985) alternative heteroskedasticity robust standard errors. Defined as sqrt(diag(n/(n-p)*HC_0) HC1_see is a cached property. When HC1_se or cov_HC1 is called the RegressionResults instance will then have another attribute het_scale, which is in this case is n/(n-p)*resid**2.
HC2_se
MacKinnon and White’s (1985) alternative heteroskedasticity robust standard errors. Defined as (X.T X)^(-1)X.T diag(e_i^(2)/(1-h_ii)) X(X.T X)^(-1) where h_ii = x_i(X.T X)^(-1)x_i.T HC2_see is a cached property. When HC2_se or cov_HC2 is called the RegressionResults instance will then have another attribute het_scale, which is in this case is resid^(2)/(1-h_ii).
HC3_se
MacKinnon and White’s (1985) alternative heteroskedasticity robust standard errors. Defined as (X.T X)^(-1)X.T diag(e_i^(2)/(1-h_ii)^(2)) X(X.T X)^(-1) where h_ii = x_i(X.T X)^(-1)x_i.T HC3_see is a cached property. When HC3_se or cov_HC3 is called the RegressionResults instance will then have another attribute het_scale, which is in this case is resid^(2)/(1-h_ii)^(2).
model
A pointer to the model instance that called fit() or results.
mse_model
Mean squared error the model. This is the explained sum of squares divided by the model degrees of freedom.
mse_resid
Mean squared error of the residuals. The sum of squared residuals divided by the residual degrees of freedom.
mse_total
Total mean squared error. Defined as the uncentered total sum of squares divided by n the number of observations.
nobs
Number of observations n.
normalized_cov_params
See specific model class docstring
params
The linear coefficients that minimize the least squares criterion. This is usually called Beta for the classical linear model.
pvalues
The two-tailed p values for the t-stats of the params.
resid
The residuals of the model.
resid_pearson
wresid normalized to have unit variance.
rsquared
R-squared of a model with an intercept. This is defined here as 1 - ssr/centered_tss if the constant is included in the model and 1 - ssr/uncentered_tss if the constant is omitted.
rsquared_adj
Adjusted R-squared. This is defined here as 1 - (nobs-1)/df_resid * (1-rsquared) if a constant is included and 1 - nobs/df_resid * (1-rsquared) if no constant is included.
scale
A scale factor for the covariance matrix. Default value is ssr/(n-p). Note that the square root of scale is often called the standard error of the regression.
ssr
Sum of squared (whitened) residuals.
uncentered_tss
Uncentered sum of squares. Sum of the squared values of the (whitened) endogenous response variable.
wresid
The residuals of the transformed/whitened regressand and regressor(s)
Methods
HC0_se
()See statsmodels.RegressionResults HC1_se
()See statsmodels.RegressionResults HC2_se
()See statsmodels.RegressionResults HC3_se
()See statsmodels.RegressionResults aic
()bic
()bse
()centered_tss
()compare_f_test
(restricted)use F test to test whether restricted model is correct compare_lm_test
(restricted[, demean, use_lr])Use Lagrange Multiplier test to test whether restricted model is correct compare_lr_test
(restricted[, large_sample])Likelihood ratio test to test whether restricted model is correct condition_number
()Return condition number of exogenous matrix. conf_int
([alpha, cols])Returns the confidence interval of the fitted parameters. cov_HC0
()See statsmodels.RegressionResults cov_HC1
()See statsmodels.RegressionResults cov_HC2
()See statsmodels.RegressionResults cov_HC3
()See statsmodels.RegressionResults cov_params
([r_matrix, column, scale, cov_p, ...])Returns the variance/covariance matrix. eigenvals
()Return eigenvalues sorted in decreasing order. ess
()f_pvalue
()f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis. fittedvalues
()fvalue
()get_prediction
([exog, transform, weights, ...])compute prediction results get_robustcov_results
([cov_type, use_t])create new results instance with robust covariance as default initialize
(model, params, **kwd)llf
()load
(fname)load a pickle, (class method) mse_model
()mse_resid
()mse_total
()nobs
()normalized_cov_params
()predict
([exog, transform])Call self.model.predict with self.params as the first argument. pvalues
()remove_data
()remove data arrays, all nobs arrays from result and model resid
()resid_pearson
()Residuals, normalized to have unit variance. rsquared
()rsquared_adj
()save
(fname[, remove_data])save a pickle of this instance scale
()ssr
()summary
([yname, xname, title, alpha])Summarize the Regression Results summary2
([yname, xname, title, alpha, ...])Experimental summary function to summarize the regression results t_test
(r_matrix[, cov_p, scale, use_t])Compute a t-test for a each linear hypothesis of the form Rb = q tvalues
()Return the t-statistic for a given parameter estimate. uncentered_tss
()wald_test
(r_matrix[, cov_p, scale, invcov, ...])Compute a Wald-test for a joint linear hypothesis. wald_test_terms
([skip_single, ...])Compute a sequence of Wald tests for terms over multiple columns wresid
()Methods
HC0_se
()See statsmodels.RegressionResults HC1_se
()See statsmodels.RegressionResults HC2_se
()See statsmodels.RegressionResults HC3_se
()See statsmodels.RegressionResults aic
()bic
()bse
()centered_tss
()compare_f_test
(restricted)use F test to test whether restricted model is correct compare_lm_test
(restricted[, demean, use_lr])Use Lagrange Multiplier test to test whether restricted model is correct compare_lr_test
(restricted[, large_sample])Likelihood ratio test to test whether restricted model is correct condition_number
()Return condition number of exogenous matrix. conf_int
([alpha, cols])Returns the confidence interval of the fitted parameters. cov_HC0
()See statsmodels.RegressionResults cov_HC1
()See statsmodels.RegressionResults cov_HC2
()See statsmodels.RegressionResults cov_HC3
()See statsmodels.RegressionResults cov_params
([r_matrix, column, scale, cov_p, ...])Returns the variance/covariance matrix. eigenvals
()Return eigenvalues sorted in decreasing order. ess
()f_pvalue
()f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis. fittedvalues
()fvalue
()get_prediction
([exog, transform, weights, ...])compute prediction results get_robustcov_results
([cov_type, use_t])create new results instance with robust covariance as default initialize
(model, params, **kwd)llf
()load
(fname)load a pickle, (class method) mse_model
()mse_resid
()mse_total
()nobs
()normalized_cov_params
()predict
([exog, transform])Call self.model.predict with self.params as the first argument. pvalues
()remove_data
()remove data arrays, all nobs arrays from result and model resid
()resid_pearson
()Residuals, normalized to have unit variance. rsquared
()rsquared_adj
()save
(fname[, remove_data])save a pickle of this instance scale
()ssr
()summary
([yname, xname, title, alpha])Summarize the Regression Results summary2
([yname, xname, title, alpha, ...])Experimental summary function to summarize the regression results t_test
(r_matrix[, cov_p, scale, use_t])Compute a t-test for a each linear hypothesis of the form Rb = q tvalues
()Return the t-statistic for a given parameter estimate. uncentered_tss
()wald_test
(r_matrix[, cov_p, scale, invcov, ...])Compute a Wald-test for a joint linear hypothesis. wald_test_terms
([skip_single, ...])Compute a sequence of Wald tests for terms over multiple columns wresid
()Attributes
use_t


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Before Change
for file in ["combined_three_roles.csv", "combined_two_roles.csv"]:
input_file = os.path.join(testdata, file)
operation = "train"
k_folds = "2"
sys.argv = ["host_footprint.py", "--operation",
operation, "--kfolds", k_folds, input_file]
instance = HostFootprint()
instance.main()
operation = "predict"
sys.argv = ["host_footprint.py",
After Change
for file in ["combined_three_roles.csv", "combined_two_roles.csv"]:
input_file = os.path.join(testdata, file)
operation = "train"
sys.argv = hf_args(tmpdir, operation, input_file)
instance = HostFootprint()
instance.main()
operation = "predict"
sys.argv = hf_args(tmpdir, operation, input_file)


Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency. It is the most popular VCS used by thousand developers.
Before the Rails Girls Summer of Code, I barely had any experience with Git and I knew only some concepts. Thanks to the Git documentation and the very friendly community over at Nextcloud, I learned many new Git commands.:).
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A link of the presentation can be found on my Personal GitHub repo. The feedback that they gave us after the event was really good, and it encourages me to continue with Git workshops and not only.


I recently swiched to the Dvorak keyboard layout and have been loving it... except for one thing. All my keyboard shortcuts are whack! Most of them now require two hands, and I still accidentally use my QWERTY shortcuts quite often.
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The documentation is intended for the convenience of blblm users based on integration of the documentation of library.
split_data <- function(data, m) {
idx <- sample.int(m, nrow(data), replace = TRUE)
data %>% split(idx)
}
For the split_data function, The first parameter is the dataset the user provided and the second parameter is the number of parts the user want to split the data into. This function will return a list of m datasets with approximated equal sizes and the sum of number of rows in these five dataset is the number of rows in the original dataset.
lm_each_subsample <- function(formula, data, n, B) {
replicate(B, lm_each_boot(formula, data, n), simplify = FALSE)
}
For the lm_each_subsample function, the formula needs to be provided by the user, and the data is the dataframe in the splited data list or in the list of files of datasets provided by the user. Argument n is the sum of total number of rows of the data list, and argument B specifies the total number of replication of bootstrap by m_each_boot(formula, data, n). It returns a list which includes B lists of estimates of coefficients and sigma.
lm_each_boot <- function(formula, data, n) {
freqs <- rmultinom(1, n, rep(1, nrow(data)))
lm1(formula, data, freqs)
}
For the function lm_each_boot, the formula needs to be provided by the user, and the data is the dataframe in the splited data list or in the list of files of datasets provided by the user. Argument n is the sum of total number of rows of the data list. The freqs generates the multinomially distributed random number vectors to use in the function lm1.
lm1 <- function(formula, data, freqs) {
environment(formula) <- environment()
fit <- lm(formula, data, weights = freqs)
list(coef = blbcoef(fit), sigma = blbsigma(fit))
}
For the function lm1, the formula needs to be provided by the user, and the data is the data list created by the function split data or by reading the list of files of datasets provided by the user. The enviroment drops the original closure of formula, otherwise the formula will pick a wrong variable from the global scope. The function lm generates the linear model with weights of argument freqs. It returns the estimated coefficients as a double and returns the sigma calculated by function blbsigma.
blbcoef <- function(fit) {
coef(fit)
}
blbsigma <- function(fit) {
p <- fit$rank
y <- model.extract(fit$model, "response")
e <- fitted(fit) - y
w <- fit$weights
sqrt(sum(w * (e^2)) / (sum(w) - p))
}
The function blbcoef extracts model coefficients from the model and returns a double of the coefficients. The function blbsigma calculated the sigma by the weighted residuals and returns it as a double.
lm2 <- function(formula, data, freqs) {
# drop the original closure of formula,
# otherwise the formula will pick a wront variable from the global scope.
environment(formula) <- environment()
X <- model.matrix(reformulate(attr(terms(formula), "term.labels")), data)
y <- as.matrix(data[, all.vars(formula)[1]])
w <- freqs
rw <- sqrt(w)
rw <- as.vector(rw)
X_1 <- rw * X
y_1 <- rw * y
fit <- fast_lm(X_1, y_1)
fit$coefficients <- as.vector(fit$coefficient)
S <- attr(terms(formula), "term.labels")
names(fit$coefficients) <- colnames(X)
sigma = sqrt(sum(w * (fit$res^2)) / fit$df.residual)
list(coef = fit$coefficients, sigma = blbsigma(fit))
}
The function lm2 is used to replace the function lm1 to return the same output but using the c++ code to perform. It uses the same arguments as function lm1 and convert the data into two matrixes, one for the explanatory variables data, and the other one for the response variable vector. It returns the estimated coefficients as a double and sigma calculated by residuals.
blblm <- function(formula, file, m = 10, B = 5000, cl = 1) {
if (class(file)=="data.frame") {
data_list <- split_data(file, m)
n = nrow(file)
}
else {
data_list <- map(file, read_csv,col_types= "dd")
n = length(vroom_lines(file))-length(file)
}
if (cl !=1) {suppressWarnings(plan(multiprocess, workers = cl))}
estimates <- map(
data_list,
~ lm_each_subsample(formula = formula, data = ., n, B = B))
res <- list(estimates = estimates, formula = formula)
class(res) <- "blblm"
invisible(res)
}
For the blblm function, user need to specify the formula, and the dataset to use or a list of file of datasets to read. If the user provides a list of filename, it does not need to specify m, the number of parts the user want the dataset to split into as the list already split the datasets into parts. The user can specify B, the number of replication of the bootstrap or uses the default number of B. If the user do not want to use the parallelization, the user can just ignore the argument cl. If the user want to use more than one CPUs in the algorithm, the user can specify the number of cores want to use. The function will return m lists and each list includes B lists of estimated coefficients and sigma.
glm_each_subsample <- function(formula, family, data, n, B) {
replicate(B, glm_each_boot(formula, family, data, n), simplify = FALSE)
}
For the glm_each_subsample function, the formula needs to be provided by the user, and the family is the error distribution and link function to be used in the model specified by the user. The data is the dataframe in the splited data list or in the list of files of datasets provided by the user. Argument n is the sum of total number of rows of the data list, and argument B specifies the total number of replication of bootstrap by m_each_boot(formula, data, n). It returns a list which includes B lists of estimates of coefficients and sigma.
glm_each_boot <- function(formula, family, data, n) {
freqs <- rmultinom(1, n, rep(1, nrow(data)))
glm1(formula, family, data, freqs)
}
For the function glm_each_boot, the formula needs to be provided by the user, and the family is the error distribution and link function to be used in the model specified by the user. The data is the dataframe in the splited data list or in the list of files of datasets provided by the user. Argument n is the sum of total number of rows of the data list. The freqs generates the multinomially distributed random number vectors to use in the function lm1.
glm1 <- function(formula, family, data, freqs) {
# drop the original closure of formula,
# otherwise the formula will pick a wront variable from the global scope.
environment(formula) <- environment()
fit <- glm(formula, family, data, weights = freqs)
list(coef = blbcoef(fit), sigma = blbsigma(fit))
}
For the function glm1, the formula needs to be provided by the user, and the family is the error distribution and link function to be used in the model specified by the user. The data is the data list created by the function split data or by reading the list of files of datasets provided by the user. The enviroment drops the original closure of formula, otherwise the formula will pick a wrong variable from the global scope. The function glm generates the generalized linear model with weights of argument freqs. It returns the estimated coefficients as a double and returns the sigma calculated by function blbsigma.
blbglm <- function(formula, family = gaussian, file, m = 10, B = 5000, cl = 1) {
if (class(file)=="data.frame") {
data_list <- split_data(file, m)
n = nrow(file)
}
else {
data_list <- map(file, read_csv,col_types= "dd")
n = length(vroom_lines(file))-length(file)
}
if (cl !=1) {suppressWarnings(plan(multiprocess, workers = cl))}
estimates <- map(
data_list,
~ glm_each_subsample(formula = formula, family = family, data = ., n, B = B))
res <- list(estimates = estimates, formula = formula)
class(res) <- "blbglm"
invisible(res)
}
For the blbglm function, user need to specify the formula, and the dataset to use or a list of file of datasets to read. If the user provides a list of filename, it does not need to specify m, the number of parts the user want the dataset to split into as the list already split the datasets into parts. If the user do not specify the error distribution, it will use the default gaussian distribution. The user can specify B, the number of replication of the bootstrap or uses the default number of B. If the user do not want to use the parallelization, the user can just ignore the argument cl. If the user want to use more than one CPUs in the algorithm, the user can specify the number of cores want to use. The function will return m lists and each list includes B lists of estimated coefficients and sigma.
The function print with the argument blbglm model will print the formula uses for the model.
sigma.blbglm <- function(object, confidence = FALSE, level = 0.95, ...) {
est <- object$estimates
sigma <- mean(map_dbl(est, ~ mean(map_dbl(., "sigma"))))
if (confidence) {
alpha <- 1 - 0.95
limits <- est %>%
map_mean(~ quantile(map_dbl(., "sigma"), c(alpha / 2, 1 - alpha / 2))) %>%
set_names(NULL)
return(c(sigma = sigma, lwr = limits[1], upr = limits[2]))
} else {
return(sigma)
}
}
The function sigma with the argument blbglm model will return the mean sigma for all estimated sigma. If the user want the confidence interval for the sigma, the user can specify the argument confident into TRUE and choose the level of the confidence for the interval.
coef.blbglm <- function(object, ...) {
est <- object$estimates
map_mean(est, ~ map_cbind(., "coef") %>% rowMeans())
}
The function coef with the argument blbglm model will return the mean coefficients for all estimated coefficients.
confint.blbglm <- function(object, parm = NULL, level = 0.95, ...) {
if (is.null(parm)) {
parm <- attr(terms(object$formula), "term.labels")
}
alpha <- 1 - level
est <- object$estimates
out <- map_rbind(parm, function(p) {
map_mean(est, ~ map_dbl(., list("coef", p)) %>% quantile(c(alpha / 2, 1 - alpha / 2)))
})
if (is.vector(out)) {
out <- as.matrix(t(out))
}
dimnames(out)[[1]] <- parm
out
}
The function confint with the argument blbglm model will return the confidence interval for the estimated coefficients. The user can specify which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered. The default confidence level is 0.95, and the user can specify the confidence level they want.
predict.blbglm <- function(object, new_data, confidence = FALSE, level = 0.95, ...) {
est <- object$estimates
X <- model.matrix(reformulate(attr(terms.formula(object$formula, data = new_data), "term.labels")), new_data)
if (confidence) {
map_mean(est, ~ map_cbind(., ~ X %*% .$coef) %>%
apply(1, mean_lwr_upr, level = level) %>%
t())
} else {
map_mean(est, ~ map_cbind(., ~ X %*% .$coef) %>% rowMeans())
}
}
The function predict with the argument blbglm model and the new data will return the predicted response value corresponding to the new data. The user can change the argument confidence to TRUE to return the confidence interval for the predicted response value. The default confidence level is 0.95, and the user can specify the confidence level they want.


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1.1.7 / 2014-06-30
- fixed a bug that occurs when a progress bar attempts to draw itself
on a console with very few columns
1.1.6 / 2014-06-16
- now prevents progress bar from exceeding TTY width by limiting its width to
the with of the TTY
1.1.5 / 2014-03-25
- updated documentation and various other repo maintenance
- updated makefile to run examples with
make
- removed dependency on readline module
1.1.4 / 2014-03-14
- now supports streams, for example output progress bar to stderr, while piping
stdout
- increases performance and flicker by remembering the last drawn progress bar
1.1.3 / 2013-12-31
- fixes a bug where bar would bug when initializing
- allows to pass updated tokens when ticking or updating the bar
- fixes a bug where the bar would throw if skipping to far
1.1.2 / 2013-10-17
- lets you pass an
fmt
and a total
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1.1.0 / 2013-09-18
- eta and elapsed tokens default to 0.0 instead of ?.?
- better JSDocs
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- added method to update the progress bar to a specific percentage
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1.0.1 / 2013-08-07
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1.0.0 / 2013-06-18
- remove .version
- merge pull request #15 from davglass/readline-osx
- on OSX revert back to terminal hack to avoid a readline bug
0.1.0 / 2012-09-19
- fixed logic bug that caused bar to jump one extra space at the end [davglass]
- working with readline impl, even on Windows [davglass]
- using readline instead of the \r hack [davglass]
0.0.5 / 2012-08-07
- add ability to tick by zero chunks - tick(0)
- fix ETA. Closes #4 [lwille]
0.0.4 / 2011-11-14
- allow more recent versions of node
0.0.3 / 2011-04-20
- changed; erase the line when complete
0.0.2 / 2011-04-20
- added custom tokens support
- fixed; clear line before writing
0.0.1 / 2010-01-03


Class WorkflowEditorModel
This class represents the workflow editor model for interacting with a workflow component within the application.
Inherited Members
System.Object.ToString()
System.Object.Equals(System.Object)
System.Object.Equals(System.Object, System.Object)
System.Object.ReferenceEquals(System.Object, System.Object)
System.Object.GetHashCode()
System.Object.GetType()
System.Object.MemberwiseClone()
Namespace: Vasont.Inspire.Models.Workflow
Assembly: Vasont.Inspire.Models.dll
Syntax
public class WorkflowEditorModel : MinimalComponentModel
Properties
| Improve this Doc View SourceAction
Gets or sets the workflow editor action type.
Declaration
public WorkflowEditorActionType Action { get; set; }
Property Value
| Type | Description |
|---|---|
| WorkflowEditorActionType |
CreateAsTemplate
Gets or sets a value indicating whether or not the workflow should be a template.
Declaration
public bool CreateAsTemplate { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Boolean |
Dirty
Gets or sets a value indicating whether the model contents have changed.
Declaration
public bool Dirty { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Boolean |
Model
Gets or sets the workflow model to be loaded into the editor interface.
Declaration
public WorkflowModel Model { get; set; }
Property Value
| Type | Description |
|---|---|
| WorkflowModel |
OriginalFileName
Gets or sets the original file name of the component for a save as allowing for the repair of any future workflow linking references.
Declaration
public string OriginalFileName { get; set; }
Property Value
| Type | Description |
|---|---|
| System.String |
ReadOnly
Gets or sets a value indicating whether the editor should be in read-only mode.
Declaration
public bool ReadOnly { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Boolean |
WorkflowType
Gets or sets the workflow type.
Declaration
public WorkflowType WorkflowType { get; set; }
Property Value
| Type | Description |
|---|---|
| WorkflowType |


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Installation and configuration of elastix telephone exchanges for large or small companies. Ideal for Call Center or the unification of IP communications within your company.
Servers
We install and configure servers: Domain, Email, cameras, printers, among others. You can manage your windows and email users instantly and without complications.
Networks
Installation, configuration and maintenance of networks. With state-of-the-art technology, your company's network will work like never before. You will be able to restrict access to unwanted pages and protect your company at the same time. You can count on: VPN, Firewall, load balancers, remote connections and more.
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Alleged 6.3bn fraud: Court grants Jang 14 days to file no-case submission
by James AbrahamJames Abraham, Jos
A Plateau State High Court sitting in Jos on Monday granted a former Governor of Plateau State, Jonah Jang, 14-day leave to file his no-case submission.
Jang and a former cashier in the office of the Secretary to the State Government, Yusuf Pam, are being tried on 17 amended charges by the Economic and Financial Crimes Commission bordering on alleged corruption and money laundering amounting to over 6.3bn during his tenure as governor
Both Jang and Pam pleaded not guilty to the charges.
Following the closure of the prosecution case after calling over 12 witnesses, Jang through his lead counsel, Mike Ozekhome (SAN), told the court on Monday that he was going to make a no-case submission stating that all the witnesses and documents exonerated his client (Jang) from financial misdeeds.
Ozekhome said that no case has been established against Jang to warrant his indictment by the court.
He told the court that they needed 14 days to write the formal no-case submission because of the volume of exhibits and documents tendered at the proceedings which they have to go through.
Before adjourning the matter to December 17,2019 for adoption of the no-case submission which the counsel to the second accused, Mr. Samuel Adeniyi Oguntuyi, also associated himself with, the trial judge, Mr. Justice Daniel Longji granted the first and second defendants 14 days to file the no-case submission while the prosecution was given 10 days to file composite response.


64
1 "Yahweh, I wish that you had come down from the sky;
you would have caused the mountains to shake with fear.
2 It would have been like fire burning up dead wood,
or like fire making water to boil.

But we did not do that; we continued to sin,
and so you became angry with us.
We have been sinning for a long time.
It is only if we continually do what you want us to do that you will save us.
6 We have all become people who are not fit to worship you;
all of the good things that we have done are only like bloodstained rags.
Because of our sins, we are all like leaves that dry up
and are blown away by the wind.
7 None of our people worships you,
and no one really tries to persuade you to help him.
You have turned away from us.
It is as though you have abandoned us, so that we keep sinning and become more and more guilty.
8 But even so, Yahweh, you are our father.
We are like clay that a potter uses,
and you created us, like a potter creates pottery.
9 Yahweh, do not continue to be angry with us;
do not keep thinking about our sins forever.

And all our other beautiful things have been ruined.
12 Yahweh, you see all those things, but it seems that you refuse to do anything to help us.
It seems that you will cause us to suffer much more."


Server Adress
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Console
Setting
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Level
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The Dark Side of The... Rainbow?
When the album is played simultaneously with the 1939 film The Wizard of Oz there are some correspondences between the film and the album.
Some moments that indicate this are:
-
When Dorothy falls off the fence in which she is balancing, the song "On the run" begins indicating the moment of suspense. Afterwards, Dorothy's grandmother appears talking, and at this moment it is possible to hear a female voice in the background of the same song;
-
When Dorothy is on the farm and she looks up, airplane noise appears in the audio; When the witch is arriving by bicycle to kidnap Dororthy's dog, they start ringing the bells of the song "Time", analogous to the ringing of bicycle bells;
-
Then the witch parks her bicycle in Dorothy's house pen and, while standing at the gate, Dorothy's grandfather slams the gate behind the witch. When the album and the film are exactly synchronized, at this knock on the gate the first note of the song "Time" is played;
-
When the phrase "Home, home again" is sung from the "Breathe" rerun, Dorothy's dog enters his bedroom window after fleeing the witch;
-
The song "The great gig in the sky" is played at the moment of the film's suspense, where a tornado approaches the house. It is possible to perceive the three stages of music in sync with the suspense scenes, the dream / faint scenes and the calm scenes;
-
The sound of the cash register at the beginning of “Money” appears exactly when Dorothy first steps on the yellow brick road, which is also the moment when the film changes from black and white to color. Another reference is the appearance of the golden fairy;
-
The moment the witch of the West appears, the word "black" is played;
-
The scene in which Dorothy encounters the scarecrow (a character who claimed to have no brain) is accompanied by the song "Brain Damage" (brain damage), and when the lyrics start to play: "the lunatic is in my head…" (the lunatic is in my head), the scarecrow begins to dance frantically like a lunatic;
-
The heartbeat at the end of the album occurs when Dorothy tries to hear the tin man's heart; The moment the witch from the west launches a fireball at Dorothy and her companions, the song screams "run!" (run);
-
The moment Dorothy meets Oz, the song "Us and Them" enters, sounding Us like Oz right when Oz's 1st image appears;
-
Several phrases of the lyrics contained in the songs coincide with the same acts being performed by the actors at the same time;
-
The length of most songs coincides precisely with the length of the scenes in the film.
The band insists that these are pure coincidences. When this fact began to go public in 1997, it aroused enormous interest in this phenomenon. A small community has spread around several sites to further explore this idea. Whether the matches are true or imagined, some fans of the album like to see "Dark side of the rainbow", as this combination is often called. Synchronization is achieved by pausing (preferably the CD version) even at the beginning and stopping the pause when the MGM lion roars a third time. The MGM presentation (that of the lion) is considered to be a "pre-film". The CD (The dark side of the moon) must start between the fade-out (of the pre-movie) and the fade-in of the exact beginning of the film. Soon, the album and the film start exactly together.
Pink Floyd members deny any relationship between the album and the film on a special MTV about the group in 2002. They say that this relationship could not be planned because they could not play the film in the studio, since at the time there were no video recorders yet.


The short of it is: I do machine learning for wireless communications problems. So far machine learning for signal processing has been particularly held back by the lack of need. In many cases, we have very good, tractable models to solve problems. Unfortunately, that tends to no longer be true when we consider the difficult problems that next generation wireless systems face. A common reason is for quantization due to low resolution data converters. The use of low resolution data converters reduces power consumption and cost, while destroying information in the received signal. Another area of interest for machine learning in signal processing is the estimation of small scale fading parameters in mmWave MIMO channels. This is actively being researched but deep learning is well positioned to provide breakthroughs in this complicated problem as well.
My first paper, which I gave a presentation on at SPAWC 2020 [video], specifically investigates the validity of deep learning for signal processing. We target the problem of estimating the carrier frequency offset of a one-bit quantized sinusoid with short block lengths and low SNR. We first show a well performing training setup, which helps to ensure the model learns effectively across a wide range of SNR. From this we found that training data should stay within 0-10dB SNR to capture the most relevant information. We also identified the effects that block length and quantization on the training data have for the final model. Finally, we proposed a convolutional neural network which is able to outperform FFT methods in both estimation error and execution time.
This work is the follow up to the previous paper. In this case we now allow for multiple sinusoids with unknown phase and amplitude to be within the received signal prior to quantization. The task is then the joint detection and estimation of the various sinusoidal components and their frequencies. This task is naturally difficult and nearly impossible with one-bit resolution, but with only two or three bits the task is learnable. We propose an end to end network which uses domain knowledge to estimate and subtract quantized sinusoids from the input for multiple signal estimation. We show that we are able to outperform Minimum Description Length (MDL) style detectors, even when using much higher resolution data for the MDL estimator. I will release the code when the paper is accepted in IEEE Trans. on Signal Processing.
This work was presented at Asilomar 2020. We now introduce more realism into the deep learning estimators from the first investigation. The most obvious first step is to allow for channel models, which we model as just random, multi-tap channels. The next step is we allow for a configurable pilot generator, which is also a neural network, to jointly optimize the pilot signal and frequency estimator. The result is that our frequency estimator becomes more robust at low SNR and is able to handle channel models without any noticeable effect. We handle the channel effects by applying Moose Algorithm within the neural networks, which repeats the training signal and looks for the rotation of phase between the pilots, removing the channel from the algorithm. We also show that the added complexity of the generator is still not significant enough to prevent near real-time operation, and it is easily retrained to handle time-variant systems.
This work is submitted to ICASSP 2021 for a special session on ML in networks. We develop a simulation configuration for computing stochastic RSRP maps using the MATLAB codebase, QuaDriGa. We then formulate the coverage and capacity (CCO) problem as a multi-objective optimization problem. For this setting, we consider a deep reinforcement learning algorithm, deep deterministic policy gradient (DDPG), and a Bayesian optimization (BO) method for determining the Pareto Frontier. We find that the BO method is over 100x more sample efficient, but leads to slightly worse configurations than DDPG. This leads the way for Bayesian optimization methods for network optimization and cellular optimization with ML.


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WebSockets test utility.
WebSockets control interface
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The Mystery of Cloomber
by Sir Arthur Conan Doyle
Chapter VII: “Of Corporal Rufus Smith and His Coming to Cloomber”
Additional Information
- Year Published: 1889
- Language: English
- Country of Origin: England
- Source: Doyle, A. C. (1889). The Mystery of Cloomber.London, England: Ward and Downey.
-
Readability:
- Flesch–Kincaid Level: 9.5
- Word Count: 3,864
- Genre: Mystery
- Keywords: 19th century literature, british literature, mystery, sir arthur conan doyle, the mystery of cloomber
- ✎ Cite This
Downloads
In making this statement I have purposely couched it in bald and simple language, for fear I should be accused of colouring my narrative for the sake of effect. If, however, I have told my story with any approach to realism, the reader will understand me when I say that by this time the succession of dramatic incidents which had occurred had arrested my attention and excited my imagination to the exclusion of all minor topics.
How could I plod through the dull routine of an agent’s work, or interest myself in the thatch of this tenant’s bothy or the sails of that one’s boat, when my mind was taken up by the chain of events which I have described, and was still busy seeking an explanation for them.
Go where I would over the countryside, I could see the square, white tower shooting out from among the trees, and beneath that tower this ill-fated family were watching and waiting, waiting and watching—and for what? That was still the question which stood like an impassable barrier at the end of every train of thought.
Regarded merely as an abstract problem, this mystery of the Heatherstone family had a lurid fascination about it, but when the woman whom I loved a thousandfold better than I did myself proved to be so deeply interested in the solution, I felt that it was impossible to turn my thoughts to anything else until it had been finally cleared up.
My good father had received a letter from the laird, dated from Naples, which told us that he had derived much benefit from the change, and that he had no intention of returning to Scotland for some time. This was satisfactory to all of us, for my father had found Branksome such an excellent place for study that it would have been a sore trial to him to return to the noise and tumult of a city. As to my dear sister and myself, there were, as I have shown, stronger reasons still to make us love the Wigtownshire moors.
In spite of my interview with the general—or perhaps I might say on account of it—I took occasion at least twice a day to walk towards Cloomber and satisfy myself that all was well there. He had begun by resenting my intrusion, but he had ended by taking me into a sort of half-confidence, and even by asking my assistance, so I felt that I stood upon a different footing with him than I had done formerly, and that he was less likely to be annoyed by my presence. Indeed, I met him pacing round the enclosure a few days afterwards, and his manner towards me was civil, though he made no allusion to our former conversation.
He appeared to be still in an extreme state of nervousness, starting from time to time, and gazing furtively about him, with little frightened, darting glances to the right and the left. I hoped that his daughter was right in naming the fifth of October as the turning point of his complaint, for it was evident to me as I looked at his gleaming eyes and quivering hands, that a man could not live long in such a state of nervous tension.
I found on examination that he had had the loose rails securely fastened so as to block up our former trysting-place, and though I prowled round the whole long line of fencing, I was unable to find any other place where an entrance could be effected.
Here and there between the few chinks left in the barrier I could catch glimpses of the Hall, and once I saw a rough-looking, middle-aged man standing at a window on the lower floor, whom I supposed to be Israel Stakes, the coachman. There was no sign, however, of Gabriel or of Mordaunt, and their absence alarmed me. I was convinced that, unless they were under some restraint, they would have managed to communicate with my sister or myself. My fears became more and more acute as day followed day without our seeing or hearing anything of them.
One morning—it was the second day of October—I was walking towards the Hall, hoping that I might be fortunate enough to learn some news of my darling, when I observed a man perched upon a stone at the side of the road.
As I came nearer to him I could see that he was a stranger, and from his dusty clothes and dilapidated appearance he seemed to have come from a distance. He had a great hunch of bread on his knee and a clasp-knife in his hand, but he had apparently just finished his breakfast, for he brushed the crumbs off his lap and rose to his feet when he perceived me.
Noticing the great height of the fellow and that he still held his weapon, I kept well to the other side of the road, for I knew that destitution makes men desperate and that the chain that glittered on my waistcoat might be too great a temptation to him upon this lonely highway. I was confirmed in my fears when I saw him step out into the centre of the road and bar my progress.
“Well, my lad,” I said, affecting an ease which I by no means felt, “what can I do for you this morning?”
The fellow’s face was the colour of mahogany with exposure to the weather, and he had a deep scar from the corner of his mouth to his ear, which by no means improved his appearance. His hair was grizzled, but his figure was stalwart, and his fur cap was cocked on one side so as to give him a rakish, semi-military appearance. Altogether he gave me the impression of being one of the most dangerous types of tramp that I had ever fallen in with.

“You’re not a beak,” he said, “too young for that, I guess. They had me in chokey at Paisley and they had me in chokey at Wigtown, but by the living thunder if another of them lays a hand on me I’ll make him remember Corporal Rufus Smith! It’s a darned fine country this, where they won’t give a man work, and then lay him by the heels for having no visible means of subsistence.”
“I am sorry to see an old soldier so reduced,” said I. “What corps did you serve in?”
“H Battery, Royal Horse Artillery. Bad cess to the Service and every one in it! Here I am nigh sixty years of age, with a beggarly pension of thirty-eight pound ten—not enough to keep me in beer and baccy.”
“I should have thought thirty-eight pound ten a year would have been a nice help to you in your old age,” I remarked.

“How much d’ye think that slash with a tulwar is worth? And my foot with all the bones rattling about like a bagful of dice where the trail of the gun went across it. What’s that worth, eh? And a liver like a sponge, and ague whenever the wind comes round to the east—what’s the market value of that?   “I know what good living is, and, by cripes! While I have a shilling in my pocket I like to spend it as a shilling should be spent. I’ve fought for my country and my country has done darned little for me. I’ll go to the Rooshians, so help me! I could show them how to cross the Himalayas so that it would puzzle either Afghans or British to stop ‘em. What’s that secret worth in St. Petersburg, eh, mister?”
“I am ashamed to hear an old soldier speak so, even in jest,” said I sternly.
“Jest, indeed!” He cried, with a great, roaring oath. “I’d have done it years ago if the Rooshians had been game to take it up. Skobeloff was the best of the bunch, but he’s been snuffed out. However, that’s neither here nor there. What I want to ask you is whether you’ve ever heard anything in this quarter of a man called Heatherstone, the same who used to be colonel of the 41st Bengalis? They told me at Wigtown that he lived somewhere down this way.”
“He lives in that large house over yonder,” said I, pointing to Cloomber Tower. “You’ll find the avenue gate a little way down the road, but the general isn’t over fond of visitors.”
The last part of my speech was lost upon Corporal Rufus Smith; for the instant that I pointed out the gate he set off hopping down the road.
His mode of progression was the most singular I have ever seen, for He would only put his right foot to the ground once in every half-dozen strides, while he worked so hard and attained such a momentum with the other limb that he got over the ground at an astonishing speed.
I was so surprised that I stood in the roadway gazing after this hulking figure until the thought suddenly struck me that some serious result might come from a meeting between a man of such blunt speech and the choleric, hot-headed general. I therefore followed him as he hopped along like some great, clumsy bird, and overtook him at the avenue gate, where he stood grasping the ironwork and peering through at the dark carriage-drive beyond.
“He’s a sly old jackal,” he said, looking round at me and nodding his head in the direction of the Hall. “He’s a deep old dog. And that’s his bungalow, is it, among the trees?”
“That is his house,” I answered; “but I should advise you to keep a more civil tongue in your head if you intend to speak with the general. He is not a man to stand any nonsense.”
“Right you are. He was always a hard nut to crack. But isn’t this him coming down the avenue?”
I looked through the gate and saw that it was indeed the general, who, having either seen us or been attracted by our voices, was hurrying down towards us. As he advanced he would stop from time to time and peer at us through the dark shadow thrown by the trees, as if he were irresolute whether to come on or no.
portrait of Sir Arthur Conan Doyle
“He’s reconnoitering!” whispered my companion with a hoarse chuckle. “He’s afraid—and I know what he’s afraid of. He won’t be caught in a trap if he can help it, the old ‘un. He’s about as fly as they make ‘em, you bet!”
Then suddenly standing on his tip-toes and waving his hand through the bars of the gate, he shouted at the top of his voice:
“Come on, my gallant commandant! Come on! The coast’s clear, and no enemy in sight.”
This familiar address had the effect of reassuring the general, for he came right for us, though I could tell by his heightened colour that his temper was at boiling point.
“What, you here, Mr. West?” he said, as his eye fell upon me. “What is it you want, and why have you brought this fellow with you?”
“I have not brought him with me, sir,” I answered, feeling rather disgusted at being made responsible for the presence of the disreputable-looking vagabond beside me. “I found him on the road here, and he desired to be directed to you, so I showed him the way. I know nothing of him myself.”
“What do you want with me, then?” the general asked sternly, turning to my companion.
“If you please, sir,” said the ex-corporal, speaking in a whining voice, and touching his moleskin cap with a humility which contrasted strangely with the previous rough independence of his bearing, “I’m an old gunner in the Queen’s service, sir, and knowing your name by hearing it in India I thought that maybe you would take me as your groom or gardener, or give me any other place as happened to be vacant.”
“I am sorry that I cannot do anything for you, my man,” the old soldier answered impressively.
“Then you’ll give me a little just to help me on my way, sir,” said he cringing mendicant. “You won’t see an old comrade go to the bad for the sake of a few rupees? I was with Sale’s brigade in the Passes, sir, and I was at the second taking of Cabul.”
General Heatherstone looked keenly at the supplicant, but was silent to his appeal.
“I was in Ghuznee with you when the walls were all shook down by an earthquake, and when we found forty thousand Afghans within gunshot of us. You ask me about it, and you’ll see whether I’m lying or not. We went through all this when we were young, and now that we are old you are to live in a fine bungalow, and I am to starve by the roadside. It don’t seem to me to be fair.”
“You are an impertinent scoundrel,” said the general. “If you had been a good soldier you would never need to ask for help. I shall not give you a farthing.”
“One word more, sir,” cried the tramp, for the other was turning away, “I’ve been in the Tarada Pass.”
The old soldier sprang round as if the words had been a pistol-shot.
“What—what d’ye mean?” he stammered. “I’ve been in the Tarada Pass, sir, and I knew a man there called Ghoolab Shah.”

Their effect upon the general was extraordinary. He fairly staggered back from the gateway, and his yellow countenance blanched to a livid, mottled grey. For a moment he was too overcome to speak. At last he gasped out:
“Ghoolab Shah’ Who are you who know Ghoolab Shah?”
“Take another look,” said the tramp, “your sight is not as keen as it was forty years ago.”
The general took a long, earnest look at the unkempt wanderer in front of him, and as he gazed I saw the light of recognition spring up in his eyes.
“God bless my soul!” he cried. “Why, it’s Corporal Rufus Smith.”
“You’ve come on it at last,” said the other, chuckling to himself. “I was wondering how long it would be before you knew me.  It’s hard to talk through a grating. It’s too much like ten minutes with a visitor in the cells.”
The general, whose face still bore evidences of his agitation, undid the bolts with nervous, trembling fingers. The recognition of Corporal Rufus Smith had, I fancied, been a relief to him, and yet he plainly showed by his manner that he regarded his presence as by no means an unmixed blessing.
“Why, Corporal,” he said, as the gate swung open, “I have often wondered whether you were dead or alive, but I never expected to see you again. How have you been all these long years?”
“How have I been?” the corporal answered gruffly. “Why, I have been drunk for the most part. When I draw my money I lay it out in liquor, and as long as that lasts I get some peace in life. When I’m cleaned out I go upon tramp, partly in the hope of picking up the price of a dram, and partly in order to look for you.”
“You’ll excuse us talking about these private matters, West,” the general said, looking round at me, for I was beginning to move away. “Don’t leave us. You know something of this matter already, and may find yourself entirely in the swim with us some of these days.”
Corporal Rufus Smith looked round at me in blank astonishment.
“In the swim with us?” he said. “However did he get there?”
“Voluntarily, voluntarily,” the general explained, hurriedly sinking his voice. “He is a neighbour of mine, and he has volunteered his help in case I should ever need it.”
This explanation seemed, if anything, to increase the big stranger’s surprise.
 “I never heard tell of such a thing.”
“And now you have found me, Corporal Smith,” said the tenant of Cloomber, “what is it that you want of me?”
“Why, everything. I want a roof to cover me, and clothes to wear, and food to eat, and, above all, brandy to drink.”
“Well, I’ll take you in and do what I can for you,” said the general slowly. “But look here, Smith, we must have discipline.  Now, don’t let me have to remind you of that again.”
The tramp drew himself up to his full height and raised his right hand with the palm forward in a military salute.
“I can take you on as gardener and get rid of the fellow I have got. As to brandy, you shall have an allowance and no more. We are not deep drinkers at the Hall.”
“Don’t you take opium, or brandy, or nothing yourself, sir?” asked Corporal Rufus Smith.
“Nothing,” the general said firmly.
“Well, all I can say is, that you’ve got more nerve and pluck than I shall ever have. I don’t wonder now at your winning that Cross in the Mutiny. If I was to go on listening night after night to them things without ever taking a drop of something to cheer my heart—why, it would drive me silly.”
General Heatherstone put his hand up, as though afraid that his companion might say too much.
“I must thank you, Mr. West,” he said, “for having shown this man my door. I would not willingly allow an old comrade, however humble, to go to the bad, and if I did not acknowledge his claim more readily it was simply because I had my doubts as to whether he was really what he represented himself to be. Just walk up to the Hall, Corporal, and I shall follow you in a minute.”
“Poor fellow!” he continued, as he watched the newcomer hobbling up the avenue in the ungainly manner which I have described. “He got a gun over his foot, and it crushed the bones, but the obstinate fool would not let the doctors take it off. I remember him now as a smart young soldier in Afghanistan. He and I were associated in some queer adventures, which I may tell you of some day, and I naturally feel sympathy towards him, and would befriend him. Did he tell you anything about me before I came?”
“Not a word,” I replied.
“Oh,” said the general carelessly, but with an evident expression of relief, “I thought perhaps he might have said something of old times. Well, I must go and look after him, or the servants will be frightened, for he isn’t a beauty to look at. Good-bye!”
With a wave of the hand the old man turned away from me and hurried up the drive after this unexpected addition to his household, while I strolled on round the high, black paling, peering through every chink between the planks, but without seeing a trace either of Mordaunt or of his sister.
I have now brought this statement down to the coming of Corporal Rufus Smith, which will prove to be the beginning of the end.
I have set down soberly and in order the events which brought us to Wigtownshire, the arrival of the Heatherstones at Cloomber, the many strange incidents which excited first our curiosity and finally our intense interest in that family, and I have briefly touched upon the circumstances which brought my sister and myself into a closer and more personal relationship with them. I think that there cannot be a better moment than this to hand the narrative over to those who had means of knowing something of what was going on inside Cloomber during the months that I was observing it from without.
Israel Stakes, the coachman, proved to be unable to read or write, but Mr. Mathew Clark, the Presbyterian Minister of Stoneykirk, has copied down his deposition, duly attested by the cross set opposite to his name. The good clergyman has, I fancy, put some slight polish upon the narrator’s story, which I rather regret, as it might have been more interesting, if less intelligible, when reported verbatim. It still preserves, however, considerable traces of Israel’s individuality, and may be regarded as an exact record of what he saw and did while in General Heatherstone’s service.


Open Source powers most of the internet, through the existence of linux, python, angular and bootstrap to name a few. With some Open Source projects having in excess of 70,000 contributors, it is necessary for there to be methods and standards in order for these projects to succeed. We will be looking at how we can best manage and deploy the great tools we have at our disposal in order to achieve better collaboration, less technical conflicts and ultimately a more inclusive project. This talk will also look at one of the biggest drivers in Open Source: it’s Community, and how best to be part of this Community both from a contributor and maintainer perspective.

## Bug Fixes
- compiler: ensure that event handlers have the correct source spans (#28055) (cffd862)
- compiler: fix two existing expression transformer issues (#28523) (09af7ea)
- core: improve global variable detection (#28679) (77eee42), closes #16545
- core: use the correct generated URL for JIT compiled components (#28055) (4f46bfb)
- core: use the correct template URL in render3 JIT compilation (#28055) (a5ea55a)
- forms: mark form as pristine before emitting value and status change events (#28395) (1df3aef), closes #28130
- router: redirect to root url when returned as UrlTree from guard (#28271) (50732e1), closes #27845
- router: set href when routerLink is used on an 'area' element (#28441) (ed0cf7e), closes #28401
## Features
- compiler: support tokenizing a sub-section of an input string (#28055) (eeb560a)
- compiler: support tokenizing escaped strings (#28055) (2424184)
- compiler-cli: no longer re-export external symbols by default (#28633) (91b7152), closes #28594 #25644 #25644
Passionate for #opensource #inclusion #bestpractices
@eddiejaoude


draw mark marktype x y size
Draws a marker of type marktype
at position
x, y
at the requested size
.
The marktype
may be one of the following:
0 - none 1 - plus sign 2 - open circle 3 - closed circle 4 - open square 5 - closed square 6 - multiplication sign 7 - open diamond 8 - open triangle 9 - closed triangle 10 - open circle with vertical bar 11 - closed circle with vertical bar


{% extends "global/Page.html" %}
{% load otree %}
{% block title %}
Summary of your earnings from Part 1 and Part 2
{% endblock %}
{% block content %}
In Part 1:
you placed {{ part_1_contribution }} tokens in the group account.
The total number of tokens in the group account was {{ part_1_total_contribution }}.
Your total earnings from Part 1 (private and group accounts) are {{ part_1_payoff }} ECUs.
In Part 2:
Your total earnings from Part 2 (private and group accounts) are {{ part_2_payoff }} ECUs.
Thus, your earnings from this experiment are {{ total }} ECUs ({{ amount_paid }} $).
Thank you for your participation!


|
| Civil Rights: Housing FAQ |
| the Department of Justice |
|
What is the Fair Housing Act?
How does the Department of Justice enforce the Fair Housing Act?
What do I do if I believe I have been the victim of illegal discrimination in housing?
Does the Fair Housing Act prohibit discrimination on the basis of a person's sexual orientation?
What is the Equal Credit Opportunity Act?
How does the Department of Justice enforce the Equal Credit Opportunity Act?
What do I do if I believe that I was the victim of an unfair credit transaction involving residential property?
What is Title II of the Civil Rights Act of 1964?
How does the Department of Justice enforce Title II?
What do I do if I believe that I have been the victim of discrimination under Title II?
Q: What is the Fair Housing Act?
A: The Fair Housing Act prohibits discrimination in housing on the basis of race, color, religion, sex, national origin, familial status, or disability by housing providers. It applies to landlords and real estate companies as well as other entities, such as municipalities, banks or other lending institutions, and homeowners insurance companies.
Return to index . . .
Q: How does the Department of Justice enforce the Fair Housing Act?
A: Under the Fair Housing Act, the Department of Justice may start a lawsuit where it has reason to believe that a person or entity is engaged in a "pattern or practice" of discrimination, or where a denial of rights to a group of persons raises an issue of general public importance. Through these lawsuits, the Department can obtain money damages, both actual and punitive damages, for those individuals harmed by a defendant's discriminatory actions, as well as prevent any further discriminatory conduct. The violator may also be required to pay money penalties to the United States.
The Department of Housing and Urban Development [“HUD”] investigates individual cases of discrimination in housing. If HUD determines that reasonable cause exists to believe that a discriminatory housing practice has occurred, then either the person complaining or the person against whom the complaint was filed may elect to have the case heard in federal court. In those instances, the Department of Justice will bring the case on behalf of the individual complainant. In addition, where force or a threat of force is used to deny or interfere with fair housing rights, the Department of Justice may begin criminal proceedings.
Finally, in cases involving discrimination in home mortgage loans or home improvement loans, the Department may file suit under both the Fair Housing Act and the Equal Credit Opportunity Act.
Return to index . . .
Q: What do I do if I believe I’ve been the victim of illegal discrimination in housing?
A: Individuals who believe that they have been victims of an illegal housing practice may file a complaint with the Department of Housing and Urban Development [“HUD”] or file their own lawsuit in federal or state court.
You must file the complaint with HUD within one year of the incident you believe to be housing discrimination.
If you choose to file your own lawsuit in federal or state court, the Act requires that you do so within two years of the incident.
Return to index . . .
Q: Does the Fair Housing Act prohibit discrimination on the basis of a person's sexual orientation?
A: When sexual orientation is the only basis of discrimination, no. However, these complaints are evaluated on a case-by-case basis to determine whether any other form of discrimination is present (such as sex or disability, for example).
Many state and local laws prohibit discrimination in housing based on sexual orientation. You should consult with your local or state civil rights enforcement agency to determine whether discrimination on this basis is protected.
Return to index . . .
Q: What is the Equal Credit Opportunity Act?
A: Under the Equal Credit Opportunity Act, a creditor may not discriminate on the basis of sex, race, color, religion, national origin, marital status, age or source of income in any credit transaction.
Return to index . . .
Q: How does the Department of Justice enforce the Equal Credit Opportunity Act?
A: The Department of Justice may start a lawsuit when it has reason to believe that a creditor is engaged in a "pattern or practice" of discrimination. Through these lawsuits, the Department can obtain money damages, both actual and punitive damages, for those individuals harmed by discriminatory actions, as well as prevent further discrimination by the defendant.
Individual complaints of discrimination are handled by the creditor's federal regulatory agency. The agencies and the types of financial institutions that they regulate are as follows:
Office of Thrift Supervision [“OTS:]:
Savings associations and federally chartered savings banks (the word "Federal" or the initials "F.S.B." appear in federal institution's name).
Comptroller of Currency [“OCC”]:
National banks, federal branches/agencies of foreign banks (the word "National" or the initials "N.A." appear in or after the bank's name).
Federal Reserve Board [“FRB”]:
Financial institutions that are members of the Federal Reserve System, except national banks and federal branches/agencies of foreign banks.
Federal Deposit Insurance Corporation [“FDIC”]:
State chartered banks that are not members of the Federal Reserve System.
National Credit Union Association [“NCUA”]:
Federal credit unions (the words "Federal credit union" appear in the institution's name).
Federal Trade Commission {“FTC”]:
Retailers, finance companies, creditors (including most mortgage companies) that aren’t assigned to another agency
Return to index . . .
Q: What do I do if I believe that I’ve been the victim of an unfair credit transaction involving residential property?
A: Individuals who believe that they have been victims of an illegal housing practice - such as the denial of a mortgage- involving credit may file a complaint with the Department of Housing and Urban Development [“HUD”].
Return to index . . .
Q: What is Title II of the Civil Rights Act of 1964?
A: This law prohibits discrimination because of a person's race, color, religion, or national origin in certain places of public accommodation, such as hotels, restaurants and places of entertainment.
Return to index . . .
Q: How does the Department of Justice enforce Title II?
A: When there is reason to believe that a person or entity has engaged in a "pattern or practice" of discrimination which violates Title II, the Department of Justice can bring a lawsuit. However, unlike lawsuits enforcing the Fair Housing Act or the Equal Credit Opportunity Act, the Department cannot obtain monetary damages for individuals in Title II cases.
Return to index . . .
Q: What do I do if I believe that I’ve been the victim of discrimination under Title II?
A: Individuals who believe that a place of public accommodation has violated Title II may file their own lawsuit in federal court. In addition, you may some rights under other federal laws, state laws, or local ordinances and should consult with your local or state civil rights enforcement agency.
Return to index . . . |
|
|


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The Game Engine You Can Trust!
Manelli Engine is used in java and is purely a java based game engine.
Manelli Engine is easy to use and easy for beginners.
Manelli Engine has very fine control over every pixel of the game.
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Identifiers
An identifier is a string or symbol, assigned to an organization by a dubbing process
The Organization Ontology uses The Identifier Ontology [https://github.com/mconlon17/identifier-ontology] to represent identifiers for organizations.
The Identifier Ontology is a small set of terms in Information Artifact Ontology (IAO) [http://www.ontobee.org/ontology/IAO] to represent identifiers, and in particular, persistent identifiers, often called PIDs. Persistent identifiers are maintained by one or more maintainers interested in the persistence of the identifier and its assignment to an entity over time.
The table below lists identifiers available in the Organization Ontology 1
See Table 4.
Term ID |
Label |
Definition |
|---|---|---|
|
crossref funder identifi |
An identifier assigned by CrossRef to an organiz |
|
dbpedia identifier |
A URL used by DBpedia to identify an entity |
|
global research organiza |
An identifier assigned and managed by Digital Sc |
|
international standard n |
An identifier for persons and organizations whic |
|
research organization re |
An identifier assigned by ROR to research organi |
|
wikidata q number |
QID (or Q number) is the unique identifier of a |
|
ringgold identifier |
The Ringgold Identifier is a unique numerical id |
Usage
To assert that an organization has an identifier, we assert the existence of the identifier of a particular type, its value/representation, and its association to the organization. We say:
x denoted_by y
y a research_orgnization_registry_identifier
y has_representation "ror-value"
Footnotes
- 1
If a needed organization identifier is not in the table, please open a Github issue with the name and source of the identifier for inclusion in subsequent releases of the Organization Ontology.


Vernam's cypher - encryption tool (ru)
This tool allows to make XOR-encrypted messages. Warning: it's not the best solution, when the key is short
Vernam's cypher - decyphering panel (ru)
This widget is used for decyphering and works as the tool above, except it won't show anything if the key is wrong.
Current key for testing: 321.
Automatically erased records demo script
This script does exactly what's written in its description.


Program It
Try It
Run Program
Step Program
Continue
Step
Continue
Restart
Stop Program
Redeal Cards
5 cards
8 cards
10 cards
12 cards
15 cards
20 cards
Ascending
Descending
Random
Run Speed:
Steps:
The Human Machine Language
Command Toolbox:
SHIFT
Hand
Right Hand
Left Hand
TO THE
Direction
Right
Left
MOVE
Hand
Right Hand
Left Hand
TO POSITION
Position
JUMP TO LINE
Line #
JUMP TO LINE
Line #
IF
Value
compare
>
<
=
≠
≤
≥
Value
SWAP
STOP
Copy code to clipboard
Title:
Line #
Instruction


Tic-Tac-Toe
it's
green's
turn.
it's
red's
turn.
green
wins!
red
wins!
it's a draw.
green
0
played
0
red
0


HTML stands for Hyper Text Markup Language.It is the standard markup language for creating Web pages. It describes the structure of a Web page The enclosing tags can make a word or image hyperlink to somewhere else, can italicize words, can make the font bigger or smaller, and so on.
Cascading Style Sheets (CSS) is a stylesheet language used to describe the presentation of a document written in HTML or XML (including XML dialects such as SVG, MathML or XHTML). CSS describes how elements should be rendered on screen, on paper, in speech, or on other media.
The World Wide Web Consortium (W3C) is an international community where Member organizations, a full-time staff, and the public work together to develop Web standards
Hypertext transfer protocol secure (HTTPS) is the secure version of HTTP, which is the primary protocol used to send data between a web browser and a website. HTTPS is encrypted in order to increase security of data transfer.The HTTPS protocol makes it possible for website users to transmit sensitive data such as credit card numbers, banking information, and login credentials securely over the internet. For this reason, HTTPS is especially important for securing online activities such as shopping, banking, and remote work.
The Domain Name System (DNS) is the phonebook of the Internet. Humans access information online through domain names, like nytimes.com or espn.com. Web browsers interact through Internet Protocol (IP) addresses. DNS translates domain names to IP addresses so browsers can load Internet resources.
An IP address is a unique address that identifies a device on the internet or a local network. IP stands for "Internet Protocol," which is the set of rules governing the format of data sent via the internet or local network.
A packet is the unit of data that is routed between an origin and a destination on the Internet or any other packet-switched network.Each packet sent includes information such as the source and destination, the protocols or identifications.
Network routing is the process of selecting a path across one or more networks. The principles of routing can apply to any type of network, from telephone networks to public transportation. In packet-switching networks, such as the Internet, routing selects the paths for Internet Protocol (IP) packets to travel from their origin to their destination. These Internet routing decisions are made by specialized pieces of network hardware called routers.
A domain name is a string of text that maps to a numeric IP address, used to access a website from client software. In plain English, a domain name is the text that a user types into a browser window to reach a particular website. For instance, the domain name for Google is ‘google.com’.
ICANN, or the Internet Corporation for Assigned Names and Numbers, is a global multi-stakeholder organization that was created by the U.S. government and its Department of Commerce.It coordinates the Internet DNS, IP addresses, and autonomous system numbers and involves the continued management of these evolving systems and the protocols that underlie them.


WITNESS LIST State Affairs Committee March 7, 2005-2:00P HB 54 For: Schneider, Michael (TX Association of Broadcasters) Toney, Doug (Texas Daily Newspaper Association and Texas Press Association) On: Reynolds, Richard F. "Dick" (Self) Smith, Tom Smitty (Public Citizen) HB 305 For: Horton, Susan (Texas Municipal League) Against: Toney, Doug (Texas Daily Newspaper Association and Texas Press Association) On: Diamond, Ann (Tarrant County District Attorney's Office) Pichinson, Michael (Texas Conference of Urban Counties) Smith, Tom "Smitty" (Public Citizen) HB 662 For: Ahmad, Janet (Home Owners for Better Building) Look, Jon (Self) Pope, Amy (Self) Pope, AIA, Tom (Self) Against: Bowling, Bobby (Self and El Paso Builders Association) Carter, Jerry (Self and Dallas Homebuilders Association) Norman, Scott (Texas Association of Builders) Tonjes, Ray (Self and Texas Association of Builders) On: Thomas, Stephen D. (TX Residential Construction Commission) HB 858 For: Rothermel, Beth Ann (County & District Clerk's Assoc.) HB 1057 For: Patterson, Jerry (Texas VLB) On: Johnson, Edward (TBPC) HJR 13 For: Schneider, Michael (TX Association of Broadcasters) Toney, Doug (Texas Daily Newspaper Association and Texas Press Association) Against: Smith, Tom Smitty (Public Citizen) On: Reynolds, Dick (Self) HJR 27 For: Hollingsworth, Lonnie (Texas Classroom Teachers Ass'n) Marsden, Julia M (League of Women Voters of Texas) Schneider, Michael (TX Association of Broadcasters) Smith, Tom Smitty (Public Citizen & Common Cause TX) Sputnik (Texas MC Rights Association) Toney, Doug (Texas Daily Newspaper Association and Texas Press Association) Greetings to Committee On: Cooley, Jimmie (Leadership Southeast Texas)


15 February 2008
The Ministry of Health in Viet Nam has confirmed a new case of human infection of H5N1 avian influenza. The case has been confirmed by the National Institute of Hygiene and Epidemiology (NIHE).
The case is a 40 -year old male from Gia Loc district, Hai Duong province. He developed symptoms on 2 February was hospitalized on 8 February and died on 13 February. The case had contact with sick and dead poultry prior to his illness. The Ministry of Health and local health units have implemented control measures and close contacts have been identified. All remain healthy and will continue to be monitored.
Of the 103 cases confirmed to date in Vietnam, 49 have been fatal.


The price includes the collected framework of logs of wild log with a rafter system and insulation pads. Also cut out openings for windows and doors according to the project. In addition, the installation of the roof and the selected materials according to the wishes of the customer.


 Get started by creating your first campaign.
{% endif %}


Eyassu
Shimelis
Projects
Autonomous Robot Navigation [E160]
Lab 1 - IR Distance Sensor Calibration [PDF]
Lab 2 - Odometry Error Characterization [PDF]
Lab 3 - Point Tracking Controller [PDF]
Lab 4 - Particle Filter Localization [PDF]
Final Project - Unscented Kalman Filter (UKF Localization)
Microprocessor Systems: Design and Applications [E155]
The MicroPs MicroPainter [Github]
Final Report: The MicroPainter [PDF]
Communication and Information Theory Final Project [E156]
Final Report: Frequency-Modulated Binary Acoustic Communication System Design [PDF]
Systems Engineering Final Project [E102]
Final Simulation Study - State-Space Control of an Inverted Pendulum [PDF]
Experimental Engineering [E80]
E80 Final Report - Avoiding Catastrophic Disassembly: Vibrations During Rocket Launch [PDF]


stalkbroker-py¶
We at Nook, Nook, and Nook Investment (TM) are excited to bring you Stalkbroker, a discord bot for tracking you and your friends’ turnip prices in Animal Crossing!
Using the Bot¶
Setting your Timezone¶
Your timezone is important for us to correctly track the prices on your island. It might be daytime inside stalkbroker’s server when it’s nighttime on your island!
To let stalkbroker know what timezone you are playing in, type:
$timezone <your timezone>
Like so:
$timezone America/Los_Angeles
Stalkbroker will confirm it heard you with a few reactions!
Timezones names are very a particular, you can find your timezone name
here, in the
'TZ database name'
column.
Try to find the most specific timezone possible – time standards are weird and hard! How we wish everyone could be on ~island time~… but the stalkmarket moves too fast for that!
Note
There are three shorthand timezones stalkbroker will recognize:
US/Pacific: PST
US/Eastern: EST
US/Central: CST
For all other timezones, see the chart referenced above.
Setting Your Current Turnip Price¶
Stalkbroker keeps a stalk ticker for your turnip price each week.
To set your current turnip price, use the following command:
$ticker 205
We’ll add a few reactions to your post to let we know we heard you.
If your bell price is high, the following bulletin will be sent to your server’s bulletin channel:
The Market Is Moving!!!
Market: @Billy (Zalack) 🍊 - Verune
The Nooks' Offer: 205
Date: Saturday Apr 24, 2020
Period: PM
Memo: 401K through the vegetable way.
@everyone
Note
The default threshold for sending out a bulletin is a sale price of 200 bells. This threshold is configurable by the server admins.
Note
We’re smart enough to know that Sunday is the day Daisey Mae comes into town. Your price that day will be set as the purchase price automatically!
Note
If you are a member of multiple servers that stalkbroker is also a member of, your bulletin will be sent to each server’s bulletins channel. That way you only need to type your update once and stalkbroker will take care of the rest.
Stalkbroker CANNOT see servers it is not invited to, so only servers which have stalkbroker installed will get these automatic updates!
Setting a Past Turnip Price¶
Say it’s 12:30 PM and (oh no!) you forgot to update your ticker in the morning! Not to fret! You can type:
$ticker 121 AM
In this case, a bulletin will not go out to your server, as it is not a current, actionable price. The market doesn’t move on yesterday’s news, as we say. However, it’s still worth setting so you can pull up a more complete ticker later.
If you missed an earlier day, you can also specify a a date:
$ticker 136 4/23 AM
Down the road, stalkbroker will be able to do price projections, and the more complete your ticker, the more accurate the projections will be!
Note
When setting a price, the price, date, and time of day (AM/PM) can appear in any order. All of the following commands are valid:
$ticker 136 4/23 AM
$ticker 136 AM 4/23
$ticker AM 136 4/23
$ticker 4/23 AM 136
etc, etc
Note
When setting the price for a sunday, Stalkbroker knows this is the the purchase price. Additionally, when retroactively setting past price, there is no need to include the AM / PM value for a sunday, though including it will not throw an error.
Getting Your Ticker¶
To look up your current week’s stalk ticker, just type:
$ticker
Stalkbroker will give you an overview of your market’s trends:
Market Report
Market: Billy (Zalack) 🍊 - Verune
Week Of: 04/19/20
Daisey'S Deal: 98
Monday Am: 68
Monday Pm: 78
Tuesday Am: 89
Tuesday Pm: 65
Wednesday Am: 110
Wednesday Pm: 115
Thursday Am: 209
Thursday Pm: 190
Friday Am: 180
Friday Pm: 120
Memo: Not just another piece of shovelware
To get a ticker for a past week just request a date from that week:
$ticker 4/5
And to get a friend’s ticker instead of yours, just tag ‘em:
$ticker @TheRealDarthVader
Warning
You cannot set a price on a friend’s ticker, only view it. Mentions on ticker commands with a bell price to update will be executed on your stalk ticker.
Getting Your Forecast¶
To get a forecast for your current week, complete with a beautiful chart:
$forecast
You’ll get a chart like so:
And the following report:
Market Forecast
Market: Billy (Zalack) 🍊 - Verune
Week Of: 05/17/20
Likely High: 642 (85% chance)
Big Spike: 642 (85% chance)
Small Spike: 214 (11% chance)
Soonest Spike: 1 day
Memo: Turn-up your profits.
Signing up for Bulletins¶
Bulletins go out when a server members price goes through the roof!  On these servers you will get added to the ‘Stalk Investor’ role, which is mentioned on bulletins.
To be removed from the role, just type:
$bulletins unsubscribe
Warning
By default, you ARE NOT subscribed to bulletins. Make sure to subscribe for those sweet, sweet turnip deals.
Installing the Bot¶
To install the bot, make sure you have permissions to add bots to your server, then click this link.
Congratulations! Your server now has cutting-edge, best-in-class investment support from Nook, Nook, and Nook Investment.
Configuring the Bot¶
Bulletins Channel¶
To set the channel you want price bulletins to come into, type this in the desired channel:
$bulletins here
Bulletins Minimum¶
To change the minimum bell price required for bulletins, invoke this command in any channel:
$bulletins minimum 310
Now bulletins will only be sent when a sale price is at or above 310 bells.
And that’s it! Let’s make some bells together.
Bulletins Role¶
When stalkbroker joins your server, it will automatically create a ‘Stalk Investor’ role and add / remove users as required based on their preference for being notified of a bulletin.
Whenever a bulletin goes out, this role is mentioned at the bottom.


Package org.apache.sis.util
Simple data objects and miscellaneous utilities.
This package provides general purpose static methods working on primitive or basic Java types
(
Characters
,
CharSequences
,
StringBuilders
,
Locales
,
Numbers
,
Classes
), arrays
(ArraysExt
) or other standard Java types
(Exceptions
).
It defines also general purpose annotations, enumerations and exceptions.
Some other noticeable services are:
ObjectConverters
, together with theObjectConverter
interface, for converting various kinds of objects.Utilities
, together with theLenientComparable
interface, for comparing objects in various ways.
- Since:
- 0.3
Defined in the sis-utility
module
-
Interface Summary Interface Description Deprecable Interface of classes for which deprecated instances may exist.Disposable A resource that can be disposed when waiting for the garbage collector would be overly conservative.Emptiable Interface of classes for which empty instances may exist.LenientComparable Interfaces of classes for which instances can be compared for equality using different levels of strictness.Localized Interface of classes for which each instance is configured for a particular locale.LocalizedException An exception which can produce an error message in the client locale.ObjectConverter<S,T> A function which converts instances of source type to instances of target type. -
Class Summary Class Description ArgumentChecks Static methods for performing argument checks.ArraysExt Static methods for simple operations on arrays and array elements.Characters Static methods working onchar
values, and some character constants.Characters.Filter Subsets of Unicode characters identified by their general category.CharSequences Static methods working withCharSequence
instances.Classes Static methods working onClass
objects.Exceptions Static methods working withException
instances.Locales Static methods working onLocale
instances.Numbers Static methods working withNumber
objects, and a few primitive types by extension.ObjectConverters Static methods for creatingObjectConverter
instances or collection views based on converters.Static Parent of SIS classes that contain only static utility methods, for documentation purpose.StringBuilders Static methods working onStringBuilder
instances.Utilities Static methods for object comparisons in different ways (deeply, approximately, etc).Version Holds a version number as a sequence of strings separated by either a dot or a dash. -
Enum Summary Enum Description ComparisonMode Specifies the level of strictness when comparing twoLenientComparable
objects for equality. -
Exception Summary Exception Description CorruptedObjectException May be thrown on attempt to use an object which has been corrupted by a previous operation.NullArgumentException Thrown when a null argument has been given to a method that doesn't accept them.UnconvertibleObjectException Thrown when an object can not be converted from the source type to the target type.UnknownNameException Thrown when an operation can not complete because a given name is unrecognized.UnsupportedImplementationException Thrown when an operation can not use arbitrary implementation of an interface, and a given instance does not met the requirement. -
Annotation Types Summary Annotation Type Description Configuration Annotates methods having a system-wide impact on the configuration of the Apache SIS library.Debug Annotates classes or methods that are provided mostly for debugging purpose.Workaround Annotates code containing workarounds for bugs or limitations in an external library.


--- layout: default title: About Me ---
Hello. I'm a developer / solution architect / code monkey, building primarily on the .net stack. I'm a Microsoft Certified Professional Developer and Technology Specialist.
In my spare time I develop open source software and write about things.


{% extends "pages/wiki/base.html" %} {% block title %} {{ page.title }} {% endblock title %} {% block content %} {{ page }}
Are you sure you want to delete {{ page.title }}? This cannot be undone.


A hash-table (from id to object) containing all objects of which the slot changes have not been written to disk yet.
A queue with the ids of all objects that have been created or modified since the last commit. The queue is in least-recently-dirtied-first order. During a commit, the objects are written to disk in the same order (this is necessary to guarantee that the garbage collector never sees an id of an object that doesn't exist on disk yet.
(defclass standard-transaction (transaction)
((id :initarg :id :reader transaction-id)
;; Dirty objects
(dirty-objects :initarg :dirty-objects
:initform (make-hash-table)
:reader dirty-objects
:documentation "A hash-table \(from id to object)
containing all objects of which the slot changes have not been written
to disk yet.")
(dirty-queue :initarg :dirty-queue
:initform (make-instance 'queue)
:reader dirty-queue
:documentation "A queue with the ids of all objects
that have been created or modified since the last commit. The queue
is in least-recently-dirtied-first order. During a commit, the
objects are written to disk in the same order \(this is necessary to
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THREE LIVES AND ONLY ONE DEATH (TROIS VIES ET UNE SEULE MORT) A film review by Steve Rhodes Copyright 1997 Steve Rhodes
RATING (0 TO ****): * 1/2
Americans are not fond of foreign films, at least not those with subtitles. Americans find reading too taxing, being unable to read and munch on popcorn at the same time I suppose. The New York Times recently reported that ninety percent of the foreign films do not even make a single million dollars in the United States. With revenues that low, you can see why few distributors want to handle foreign films and even art houses shun most of them. People in the movie business are there to make a profit, not a statement.
The excitement of foreign films comes in their ability to experiment and to approach subjects in a way unlike that of Hollywood's. Sometimes, as in the recent THE POSTMAN, NELLY AND MONSIEUR ARNAUD, FRENCH TWIST, THE PROMISE and THE STARMAKER, they are quite successful, and other times, such as CEMETERY MAN, they are so bad as to be almost completely unwatchable.
Using the format of multiple short stories woven together as Eric Rohmer did this year in his wonderful RENDEZVOUS IN PARIS, highly prolific, European director Raul Ruiz brings us THREE LIVES AND ONLY ONE DEATH. The interest is provided by the casting of the great Marcello Mastroianni as a character in each of the stories. The good news is that the script by Pascal Bonitzer and Raul Ruiz takes risks, but the bad news is that it rarely works. I have never seen any of the other films by director Ruiz, and based on this one, I am not motivated to try any more.
Pierre Bellemare is a radio announcer who narrates the introduction of each of the short stories. In the first, Marcello Mastroianni plays Mateo Strano. While waiting in line, he acts strangely. When Andre (Feodor Atkine) turns to him, Mateo apologizes with, "Excuse me. I can usually control my hilarity."
It seems Mateo is so lonely that he pays people for their time so he can talk to them. Andre reluctantly accepts his money, but gets more than he bargained for. It seems that Mateo is the ex-husband of Andre's wife Maria (Marisa Paredes), whom Mateo has not seen in twenty years.
 It ends strangely in a gory scene that could have been lifted out of the awful horror show CEMETERY MAN. The tale is slow, and its only charm is the acting by Mastroianni. The story is so dispassionately and almost clinically told that there is little to draw in the viewers.
The second and more bizarre story has Professor George Vickers (Mastroianni) as a teacher at the Sorbonne. What does he teach? "Negative anthropology." Don't ask.
As the professor is walking up the steps to give his big lecture at a conference, he gets the urge to leave. He spends the night in a graveyard, and the next morning he decides to become a beggar. The show explains this by telling us that as a beggar, and as a successful one I should point out, he is happier than ever. One day he falls for a prostitute named Tania (Anna Galiena) who is also the president of a large company. From there the story manages to get even more unbelievable.
If this reivew has not already made you stop and go immediately to see this strange film, let me tell you just a bit more.
The announcer explains, "extreme happiness is a form of misery," in the introduction to the third story. In it, Mastroianni plays a butler to a poor couple, Martin (Melvil Poupaud) and Cecile (Chiara Mastroianni). It seems they have inherited a large chateau with the only provision of the will being they must never fire the butler (Mastroianni).
For completeness sake, let me say that the last story has Mastroianni playing rich businessman Luc Allamand.
THREE LIVES AND ONLY ONE DEATH is willing to take risks, and it has a talented lead. More than that, I can think of nothing to recommend the film. At least it is never boring.
THREE LIVES AND ONLY ONE DEATH runs too long at 2:03. It is in French with English subtitles. The film is not rated, but the gore in the first part and the mature themes might get it an R. If so, it is a soft R. The show would be fine for teenagers. There is little in the show worth seeing, so I give it a thumbs down and * 1/2.
**** = One of the top few films of this or any year. A must see film. *** = Excellent show. Look for it. ** = Average movie. Kind of enjoyable. * = Poor show.  Totally unbearable.
REVIEW WRITTEN ON: December 12, 1996
Opinions expressed are mine and not meant to reflect my employer's.
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