text:
\section{Introduction}
Understanding the evolutionary process of species is an important task
in phylogenetic comparative studies. \citet{felsenstein_1985} is the
first to introduce Brownian Motion (BM) to model the evolution of a continuous trait.
BM models have been used in many evolutionary studies, such as flower size
evolution (\citealp*{davis_latvis_nickrent_wurdack_baum_2007}), genome size evolution \citep{beaulieu2012modeling}, the
spread of HIV-1 in central Africa
(\citealp*{gill_tung_ho_baele_lemey_suchard_2016}), and mammalian life
history traits
(\citealp*{hassler_tolkoff_allen_ho_lemey_suchard_2020}). \citet{hansen_1997}
proposed to use an Ornstein-Uhlenbeck (OU) processes to model evolution with natural selection. Unlike the
BM process, the variance of the OU process in traits is bounded, which is
more realistic (\citealp*{butler_king_2004}). Therefore, OU processes
are now widely used in many evolutionary studies, including character
displacement in Lesser Antillean Anolis Lizards
(\citealp*{butler_king_2004}), and HIV-1 heritability
(\citealp*{bastide_ho_baele_lemey_suchard_2021}).

\citet{butler_king_2004} formulate the multiple optima OU model for
adaptive evolution in which optima differ between branches, and remain
constant along an evolutionary path until discrete events where
changes in selective regime occur. They use hypothesis testing to
test whether the optima are different between groups. The changes can
be modeled as shifts in the parameters of the OU processes. The shifts in
optima are considered to be correlated with abrupt environmental
changes (\citealp*{losos_2011};
\citealp*{mahler_ingram_revell_losos_2013}). For example,
\citet{jaffe_slater_alfaro_2011} investigate the relationship between
the difference in optimal body sizes and habitat changes for
turtles. Therefore, by detecting shifts in optima based on observed
traits, we can get knowledge of unobserved historical environmental
changes and better understand the evolutionary process of species. Our
goal here is to use statistical models to detect the evolutionary
shifts: where the shifts have occurred and the size of the
shifts.

There are some existing approaches to address this
problem. \citet{uyeda_harmon_2014} propose a Bayesian framework to
detect shifts in the selective optimum of OU models. However, results
of Bayesian approaches are deeply influenced by prior distributions
and the computation cost is relatively high.  We will focus on
frequentist approaches in this paper.  \citet{ho_ane_2014} illustrate
the limitation of traditional model selection criteria (AIC, BIC) in
the shift detection task and propose to use forward-backward
selection with modified BIC (\citealp*{zhang_siegmund_2006}).
\citet{khabbazian_kriebel_rohe_ane_2016} formulate
the shift detection problem into a variable selection problem and
combine the OU model with LASSO to detect the shift points, which
they implement in the $\ell$1ou \texttt{R} package.
\citet{bastide_mariadassou_robin_2016} develop a maximum likelihood
estimation procedure based on the EM algorithm (implemented in the \texttt{R} package PhylogeneticEM).

Recently, ensemble methods have been widely applied to variable
selection problems (\citealp*{lee_leu_2011};
\citealp*{piao_piao_park_ryu_2012};
\citealp*{mera-gaona_lopez_vargas-canas_2021}). Ensemble feature
selection can add more diversity to selected variables and produce
robust variable selection results (\citealp*{pes_2019}).
In this paper, we propose an ensemble variable selection method for
shift detection and compare it with existing methods implemented in the
PhylogeneticEM and $\ell$1ou packages. We have implemented our method
in a new \texttt{R} package, \texttt{ELPASO} (Ensemble LASSO for Phylogenetic Analysis of Shifts with OU). It is available at \url{https://github.com/WenshaZ/ELPASO}.

The following sections are organized as follows. Section \ref{sec2} introduces
the problem formulation of the shift detection task as a variable
selection problem, and presents the methodology of $\ell$1ou,
PhylogeneticEM and our ensemble method.  Section \ref{sec3} presents our comparison results on simulated data under OU models. In order to better evaluate the performance, we use three different
criteria: true positive and false positive shifts detected, predictive log-likelihood on a test data set, and adjusted rand index. Section \ref{sec4}
discusses the effect of shift position and tree shape on the performances of detection methods.
Section \ref{sec5} compares the methods when the model assumptions are not satisfied.
Previous simulation studies often ignore this scenario. However, in practice, the
model assumptions are usually violated.
Section \ref{real_data} presents the case study results on Anolis Lizard data.
Finally, Section \ref{sec7} provides conclusions and discussion.

\section{Shift detection for trait evolution models} \label{sec2}

\subsection{Trait evolution models}
The trait evolution process on a phylogenetic tree describes the
changes in traits through generations. Each species has a certain
value of the trait. And the trait values of different species are
correlated because of their shared ancestry which is represented by a phylogenetic tree. We only observe the trait values at the tips of the tree.
In this paper, the tree is assumed to be separately estimated from
sequence data, and is treated as known.

Trait evolution models are used to model
how the trait values change over time. Brownian Motion and
Ornstein–Uhlenbeck are two commonly used models to model the evolution of continuous traits.
Let $\mathrm{Y}$ denote the vector of observed trait values
at the tips, $Y_i$ as the trait value of taxon $i$.
These two models assume that conditioning on the trait value of a parent, the evolutionary processes of sister species are independent.
So we only need to specify the model on one branch. For a single branch, we let $Y(t)$ denote the trait value at time $t$.

\subsubsection*{Brownian Motion Model}
\citet{felsenstein_1985} proposed to use Brownian motion (BM) to model
the evolution of continuous traits over time.  Trait values of sister
lineages start at the trait value of their most recent common ancestor
and evolve independently following a BM model. The result of this
model is that the correlation between the trait values of two species
depends only on the evolution time they shared.  Under this model, the
observed trait values $\mathrm{Y}$ follow a multivariate Gaussian
distribution. For an ultrametric tree of height $1$, each $y_i$ has mean
$\mu_0$ and variance $\sigma^2$, and the covariance between $y_i$ and
$y_j$ is $\sigma^2 t_{ij}$, where $t_{ij}$ is the shared evolution
time between species $i$ and $j$.

\subsubsection*{Ornstein–Uhlenbeck Model}

The variance of the BM model is unbounded, which is considered
unrealistic.
The OU model \citep{hansen_1997}, on the other hand, incorporates a selection force that pulls the trait value toward a selective optimum $\theta$. This model is preferable to the BM model because of its more realistic assumptions.
An OU process $Y(t)$ is defined by the following stochastic differential equation
\begin{linenomath*}
\begin{equation} dY(t)=\alpha[\theta(t)-Y(t)]dt+\sigma dB(t), \end{equation}
\end{linenomath*}
where $ dY(t)$ is the infinitesimal change in trait value; $B(t)$ is a standard BM; $\sigma^2$ measures the intensity of random fluctuation; $\theta(t)$ is the optimal value of the trait at time $t$; and $ \alpha \geq 0$ is the selection strength. When $\alpha = 0$, the OU process is the same as a BM. We assume that $\alpha$ and $\sigma$ are constant.

\subsection{Shift detection as a linear model selection problem}
For the OU model, the assumption that the optimal value $\theta(t)$ is
constant throughout the tree is not realistic, as different trait
values are suited for different environments and evolutionary
strategies. A more practical model allows the optimal value
$\theta(t)$ to shift at certain positions on the
tree.
\citet{hansen_1997} proposed a heterogenous OU model to allow different optimal values on different branches.
Shifts are noncontinuous changes in the optimal value during the evolution process.
A shift on a branch of the phylogenetic tree would influence
all the species under that branch. Our goal here is to find the
positions of shifts and estimate the changes in optimal trait value,
$\theta$, at the shifts. We assume that the optimal value on one
branch is constant. Let $\theta_b$ denote the optimal value on branch
$b$ and $t_b$ the age of the beginning of branch $b$. Let $T$ denote
the age of the root node. We only consider ultrametric trees in this shift detection task.
Let $\pa(b)$ denote the parent edge of $b$
and $\nodeend(b)$ the end node of $b$. Thus, $\triangle \theta_b =
\theta_{\pa(b)}-\theta_b \neq 0$ means that a shift in optimal value
occurred on branch $b$.

Let $\mathbf{Y}$ denote the observed trait values at the tips, $Y_i$
denote the trait value of taxon $i$ and $Y_0$ denote the trait value of
the root node. Under the OU process, $\mathbf{Y}$ follows a
multivariate normal distribution. The mean of each random variable
$Y_i$ is (\citealp*{hansen_1997}):

\begin{equation}
   E(Y_i) = Y_0e^{-\alpha T}+\sum_{b \in \pathm(\textrm{root},i)}\left(e^{-\alpha t_{\nodeend(b)}}-e^{-\alpha t_b}\right)\theta_b
\end{equation}

The covariance between $Y_i$ and $Y_j$ is \citep{ho2013asymptotic}:

\begin{equation}
\label{eqn:covariance}
    \Sigma^{(\alpha)}_{ij}=\left\{
\begin{array}{rcl}
\sigma^2e^{-\alpha d_{ij}}(1-e^{-2\alpha t_{ij}})/(2\alpha)       &      & \textrm{fixed root model}\\
\sigma^2e^{-\alpha d_{ij}}/(2\alpha)     &      & \textrm{random root model}
\end{array} \right.
\end{equation}
where $t_{ij}$ is the shared time between species $i$ and $j$, and $d_{ij}$ is the
distance between $i$ and $j$.
In the random root model, the trait value at the root is assumed to follow the stationary distribution.
To transfer the shift detection problem into
a regression problem, we can rewrite the mean of $Y_i$ as \citep{khabbazian_kriebel_rohe_ane_2016}:

\begin{equation}
    \begin{aligned}
\label{eqn:trait-mean}
    E(Y_i) &= Y_0e^{-\alpha T}+\sum_{b \in \pathm(\textrm{root},i)}\left(e^{-\alpha t_{\nodeend(b)}}-e^{-\alpha t_b}\right)\theta_0\\
    &+\sum_{b \in \pathm(\textrm{root},i)} \sum_{b' \in \pathm(\textrm{root},b)}\left(e^{-\alpha t_{\nodeend(b)}}-e^{-\alpha t_b}\right)\triangle\theta_{b'}\\
    &=Y_0e^{-\alpha T} + (1-e^{-\alpha T})\theta_0 + \sum_{b' \in \pathm(\textrm{root},i)} \sum_{b \in \pathm(b',i)}\left(e^{-\alpha t_{\nodeend(b)}}-e^{-\alpha t_b}\right)\triangle\theta_{b'} \\
    &= Y_0e^{-\alpha T} + (1-e^{-\alpha T})\theta_0 + \sum_{b' \in \pathm(\textrm{root},i)}(1-e^{-\alpha t_{b'}})\triangle\theta_{b'}
    \end{aligned}
\end{equation}
Let $\beta_0 = Y_0e^{-\alpha T} + (1-e^{-\alpha T})\theta_0$ and $\beta_b = (1-e^{-\alpha t_{b}})\triangle\theta_{b}$. In this way, the shift detection problem under the OU model can be converted to a linear model selection problem. The trait values at tips can be written as:

\begin{equation}
   \mathbf{Y} =\beta_0\mathbf{1}+\sum_b\beta_b \mathbf{X_b}+\mathbf{\epsilon}
\end{equation}

where $X_b$ is a vector defined by $X_{bi}=0$ if taxon $i$ is not under
branch $b$, and $X_{bi}=1$ if the taxon $i$ is under branch $b$, and
$\epsilon$ follows a normal distribution with mean 0 and covariance
matrix $\Sigma^{(\alpha)}$, which is given by Equation~\ref{eqn:covariance}
The main task is to select the branches that have $\beta_b \neq 0$.

\subsection{$\ell$1ou}
\citet{khabbazian_kriebel_rohe_ane_2016} propose a phylogenetic LASSO
method to detect shifts in optimal trait value under OU models.  To remove the
influence of the covariance matrix, they conduct a transformation
before model selection:

\begin{equation}
\Sigma_{\alpha}^{-1/2}\mathbf{Y} =\beta_0\Sigma_{\alpha}^{-1/2}\mathbf{1}+\Sigma_{\alpha}^{-1/2}\mathbf{X}\mathbf{\beta}+\Sigma_{\alpha}^{-1/2}\mathbf{\epsilon}
\end{equation}

Where $\mathbf{\beta}$ denotes the vector of $\beta_b$ and $\mathbf{X}$ denotes the design matrix, the $b\text{th}$ column of $\mathbf{X}$ is $\mathbf{X_b}$. After data transformation, the error terms $\Sigma_{\alpha}^{-1/2}\epsilon$ become a vector of independent standard normal random variables. The LASSO solution is to minimize the least squares with $\ell 1$ penalty. The loss function is given by

\begin{equation}
    \frac{1}{2}\|\Sigma_{\alpha}^{-1/2}\mathbf{Y}-\beta_0\Sigma_{\alpha}^{-1/2}\mathbf{1}-\Sigma_{\alpha}^{-1/2}\mathbf{X}\mathbf{\beta}\|^2+\lambda\|\beta\|_1
\end{equation}

They use the package \texttt{lars} to estimate $\beta$ for every
$\lambda$ value, and conduct backward selection based on the model
selection criterion pBIC (see Section 2.6 for more details about pBIC)
using the models selected for each $\lambda$ value as a starting
point. They then use the same criterion to select from among the
models found for different values of $\lambda$.

The above process is based on a given $\alpha$ value.
\citet{khabbazian_kriebel_rohe_ane_2016} use the following procedure to obtain the estimation of $\alpha$. Firstly, they set $\alpha=0$ and run the
variable selection for this value of $\alpha$. They then refit
$\alpha$ with the selected variables. They repeat the selection step
for the new $\alpha$, and choose the model with the best criterion score
from among these models. Because of the use of LASSO, the computation
speed is faster than previous shift detection tools, including SURFACE (\citealp*{ingram_mahler_2013}) and bayou (\citealp*{uyeda_harmon_2014}).
Their implementation of the method is available in the \texttt{R} package $\ell$1ou.

\subsection{PhylogeneticEM} \label{phyloEM}
\citet{bastide_ane_robin_mariadassou_2018} introduce a framework which
treats phylogenetic analysis as a missing data problem, allowing the
usage of the EM algorithm.  They set $\mathbf{\tau} = (\sigma,\alpha,
\mathbf{\beta})$ as the vector of all the parameters to estimate, and
$\textbf{X = (Z,Y)}$ as the trait values of both internal and external
nodes.
$\textbf{Z}$ is
the vector of trait values of internal nodes, $\textbf{Y}$ is the
vector of trait values of external nodes.

They assume the number of shifts is fixed and use the EM algorithm to
estimate the parameters by maximizing the log likelihood
$\log{p_\tau(\mathbf{Y})}$.  The EM algorithm is based on the
decomposition:
\begin{equation}
    \log{p_\tau(\mathbf{Y})} = E_\tau[\log{p_\tau(\mathbf{Z,Y})}|\tau]-E_\tau[\log{p_\tau(\mathbf{Z|Y})}|\mathbf{Y}]
\end{equation}
The difficulty with the maximization, in this case, comes from the fact
that the locations of shifts on the branches are discrete
variables. They used a Generalized EM (GEM,
\citet{dempster_laird_rubin_1977}) to conduct the maximization. The
complexity for this is $O(n^k)$ where $k$ is the number of shifts.

The above process is based on the assumption that the number of shifts
$k$ is fixed. They estimate the parameters with $k = 1, ...., K$,
where $K$ is the given maximum number of shifts. They then conduct
model selection on $k$ based on penalized least squares:
\[
\text{PLS} =
\left(1+\frac{\text{pen}(k)}{n-k-1}\right)\sum_{i=1}^p\|Y_i-\hat{Y_i}\|^2
\]
where $\hat Y_i$ is the predicted trait value of taxon $i$ given by the
model with $k$ shifts. The penalty term $\textrm{pen}(k)$ if given by
\[
\text{pen}(k) =
A\frac{n-k-1}{n-k-2}\text{EDkhi}\left[k,n-k-2,\frac{(k+1)^2}{|S_k^{PI}|}\right]
\]
where $|S_k^{PI}|$ denotes the number of parsimonious identifiable sets of
locations of k shifts and $A$ is a constant which the authors fixed at
1.1 based on simulation results.  The EDkhi function
(\citealp*{baraud_giraud_huet_2009}) is defined as follows:
\[
\textrm{DKhi}(D,N,x)=
\frac{1}{\text{E}(X_D)}\text{E}\left[\left(X_D-x\frac{X_N}{N}\right)_+\right]
\]
where $D$ and $N$ are positive integers, $X_D$ and $X_N$ are independent chi-squared random variables with
$D$ and $N$ degrees of freedom respectively.  Then, for $0<q\leq1 $,
$\textrm{EDkhi}(D,N,q)$ is defined as the unique solution to
\[
\text{Dkhi}[D,N,\text{EDkhi}[D,N,x]] = q.
\]According to
\citet{baraud_giraud_huet_2009}, the penalty used here bounds the risk
of the selected variables, and gives non-asymptotic guarantees. The
implementation of this method is available in the \texttt{R}
package \texttt{PhylogeneticEM}.

\subsection{Ensemble method}
The framework of the ensemble variable selection model for shift
detection consists of two phases. Firstly, we apply LASSO on a number of random subsamples of the data.
For each subsample, we obtain
a ranking of the variables based on the largest penalty $\lambda$ for
which the variable is selected by LASSO. We aggregate the rankings
from each subsample into an overall variable ranking. Secondly, we
use this ranking as a basis for a variable selection method.

The foundation of ensemble learning is to combine the results of
multiple models. The idea is that combining the results of several
models will obtain better results by reducing the model variance and
bias. Bagging and boosting are the two most commonly used ensemble
models. There has been substantial recent work on the use of ensemble
models for feature selection.
\citet{bolon-canedo_alonso-betanzos_2018} summarize the different
types of ensemble methods that are used in feature selection. We here
use a homogenous scheme for the ensemble. That is, we firstly take
subsamples from the training dataset, then apply LASSO to each
subsample. LASSO provides a solution path by varying the penalty
size. Therefore, for each subsample, a variable ranking is produced
where variables are ranked in decreasing order of the largest penalty
for which they are selected. Aggregating the ranking sequences from
all the subsamples, we can get the overall ranking for all the
variables. The process is shown in Figure~\ref{Fig1}.
\begin{figure}
  \centering
    \includegraphics{ensemble.png}
     \caption{\label{Fig1}The model structure of ensemble method for shift detection}
\end{figure}

There are several choices for how we aggregate the rankings from the
different subsamples into a single overall ranking. For example,
geometric mean, arithmetic mean, median, and so on. It is possible to apply
these aggregation methods either to the ranking or to the values of
the penalty $\lambda$. We suggest using the first quantile of the
ranking to aggregate the results because the first quantile is robust
to outliers. In a few subsamples, a shift may be ranked very low,
possibly because the taxa that distinguish it from other shift
positions are not included in the subsample. In these cases, the rank
might be very large, and therefore have a large influence on the
geometric or arithmetic mean. Similarly, the first quantile help improving our ability to distinguish between shifts and surrogates. It is common for only one of the
surrogate variables to be ranked highly, and the shift to have a low
rank. This can cause the median rank of the true shifts to be low.

After obtaining the aggregated ranking, we use model selection to
choose the final combination of variables. Forward selection, backward
selection, and stepwise selection are potential approaches.
Forward selection starts with the null model, and sequentially adds
variables in the ranked order, starting with the highest ranked, as
long as the model score is better than the previous model. Backward
selection starts with the full model, and sequentially removes
variables in rank order, starting with the lowest ranked, as long as
the model score after removing each variable is better than the
previous model. Stepwise selection consists of one forward
selection pass followed by one backward selection pass, starting with
the model selected by forward selection. Firstly include the variables
that improve the model score then remove the variables whose removal
further improves the model score. From the simulation results,
stepwise selection performs best.

Our procedure to estimate $\alpha$ and $\sigma^2$ is similar to
$\ell$1ou.
\begin{enumerate}
    \item Fit a null BM phylogenetic regression model on the dataset. Get the initial estimate of $\sigma^2$.
    \item Use $\alpha=0$ and the $\sigma^2$ value from the first step to calculate the covariance matrix and apply the ensemble method selection procedure.
    \item Fit the phylogenetic regression model
      with the selected variables in Step~2 and

      get new estimates of $\alpha$ and $\sigma^2$.
    \item Repeat Step~2 and Step~3 once more.
    \item Select the model with the best model criterion score
\end{enumerate}

\subsection{Model selection criteria}
For all the models, a criterion is used to conduct model selection for the number of shifts.
AIC and BIC are most the commonly used criteria in model selection
problems. \citet{ho_ane_2014} showed that using AIC as the criterion
may lead to model
overfitting. \citet{khabbazian_kriebel_rohe_ane_2016} present a new
criterion pBIC including a phylogenetic correction. The traditional
BIC is given by:
\[
\BIC(M_k) = -2\loglik(M_k)+(2k+3)\log(n)
\]
where $n$ is the number of taxa, $k$ is the number of shifts selected, $M_k$ is the estimated model. $2k+3$ is the number of parameters: each shift location and magnitude is counted as a parameter and there are 3 general parameters ($\beta_0$, $\alpha$, and $\sigma$).
The phylogenetic BIC proposed by \citet{khabbazian_kriebel_rohe_ane_2016} is given by:
$$\pBIC(M_k) = -2\loglik(M_k)+2k\log(2n-3)+2\log(n)+\log
\text{det}((X_{M_k}^{\hat\alpha})^{T}v
\Sigma_{\alpha}^{-1}X_{M_k}^{\hat\alpha}) $$
where
$X_{M_k}^{\hat\alpha}$ is the matrix $X^{\alpha}$ with only the
columns corresponding to the k selected shifts, and $v$ is the
observed trait variance.
The penalty for the shift position is $2k\log(2n-3)$.
The penalty for shift magnitudes and the intercept are shown in the last term.
PhylogeneticEM uses penalized least squares for model selection;
details are in Section~\ref{phyloEM}.

\section{Simulation studies}
\label{sec3}

We conduct simulations to compare PhylogeneticEM, $\ell$1ou (pBIC/BIC)
and ensemble LASSO (pBIC/BIC). The most direct method for comparison is to compare how many true shifts the methods detect and
how many wrong shifts that are selected by them. However, the OU model is not completely identifiable
\citep{bastide_mariadassou_robin_2016, ho_ane_2014, khabbazian_kriebel_rohe_ane_2016}, and even if the selected
shifts are not equivalent to the true model, a good argument can be
made that selecting a close surrogate shift is preferable to failing
to select the shift at all. In these cases, the true positive versus false positive curve might misrepresent the performance, since neither
method has a true positive, but the method that selects the surrogate
has a false positive, and so is deemed to have performed worse, even
though selecting the close surrogate is arguably more
correct. Therefore, we include two more measurements in comparison:
predictive log-likelihood and adjusted rand index. The idea of
predictive log-likelihood is to compare the prediction accuracy on
test data, of the selected models from different methods. Adjusted
rand index evaluates how similar the clustering of the selected
model is to the clustering of the true model. We can get a more
comprehensive understanding of the characteristics, strengths,
limitations of the methods by combining the three different
measurements.

We simulate a number of scenarios with varying numbers of shifts and
signal sizes. We simulate datasets under OU models along the 100-taxon
Anolis lizards' tree (\citealp*{mahler_ingram_revell_losos_2013}). We
compare the methods on scenarios with 3, 7, or 12 shifts. Data were
simulated according to the shifts in Figure~\ref{Fig2}. For each scenario, we
set $\alpha = 1$ and $\sigma^2 = 2$ so that $\frac{\sigma^2}{2\alpha}
= 1$. We simulate under seven true signal sizes: $\beta=0.2, 1, 1.5, 2, 2.5, 3$ and
$5$. In each simulation, all shifts have the same value of $\beta$.

\subsection{True positive versus false positive}

\begin{figure}
  \centering
    \includegraphics[width=\textwidth]{simulation_shifts.png}
     \caption{\label{Fig2}Tree used in simulations to compare the precision and recall of different methods. The shifts positions are indicated by asterisks. Different colours indicate different optimal values for the trait.}
\end{figure}

We first compare the methods by true positive versus false positive
curves. True positive is the number of true shifts detected by the
model. False positive is the number of shifts that are not simulated
but which are wrongly detected by the model. If two models have similar false
positive values, the one with a higher true positive value is
considered to have a better performance. If one model has a higher true
positive and higher false positive than another, there is no
obvious conclusion about which model is better. It becomes a trade-off
problem between precision and recall.

Figure~\ref{Fig3} shows the average true positive versus false
positive curve from 200 simulations in each scenario. Each point in
Figure~\ref{Fig3} represents the mean of true positive and mean of
false positive values. From the simulation results, $\ell$1ou+pBIC is
usually the most conservative method, with both lowest true positive
and false positive. For example, in the simulation of 7 shifts and
$\beta=2$, $\ell$1ou+pBIC on average detects under 2
shifts. $\ell$1ou+BIC is usually the least conservative
method. Ensemble LASSO provides more balanced choices between those
two methods. In most simulations, ensemble LASSO with BIC and pBIC
have both higher true positive and false positive compared to
$\ell$1ou+pBIC and have both lower true positive and false positive
compared to $\ell$1ou+BIC. Furthermore, in some situations, ensemble
LASSO methods have a better performance compared to $\ell$1ou. For
example, in the simulation of 7 shifts and $\beta=2$, ensemble
LASSO+pBIC has higher true positive and lower false positive compared
to $\ell$1ou+pBIC and ensemble LASSO+BIC has higher true positive and
lower false positive compared to $\ell$1ou+BIC. PhylogeneticEM is even
more conservative than $\ell$1ou+pBIC when the signal sizes are
small. It performs well where there are only 3 true
shifts. However when the number of shifts and the coefficient sizes
are large enough, PhylogeneticEM performs poorly compared to other
methods, including more false positive and fewer true positive
variables.

\begin{figure}
  \centering
    \includegraphics[width=\textwidth]{1-TPFP.png}
 \caption{\label{Fig3}True positive numbers versus False positive numbers with
   3,7,12 shifts.}
\end{figure}

\subsection{Predictive log-likelihood}

For each simulation, we generate $1000$ test datasets and $200$ training
datasets.
We calculate the mean of log likelihood values over $1000$
test datasets using the estimated shifts from training sets. When
the results of a method give higher predictive log-likelihood value,
it indicates that the method performs better at predicting the trait
values. Figure~\ref{Fig4} shows the mean of average log likelihood values over
$1000$ test datasets with different numbers of true shifts and
coefficient sizes.

From the simulation results, when the size of coefficients are very
small or very large, methods with pBIC have a higher prediction log
likelihood value. When coefficient sizes are very small, methods with
pBIC are very strict and tend to select nearly no shifts. In these
scenarios, the signal sizes are so small that the null model has a
higher prediction likelihood compared to the true model. When
coefficient sizes are very large, all the methods can detect almost
all the true shifts, while the methods with BIC might include more
false positive shifts. Conversely, when the coefficient sizes are in
the middle of the range, methods with BIC have a better performance in
terms of prediction accuracy. PhylogeneticEM is quite conservative,
with high predictive log-likelihood when the signal sizes are
small. And in most cases, the performance of PhylogeneticEM is between
that of the pBIC and BIC methods. The difference between the ensemble
method and $\ell$1ou is smaller than the difference between BIC and
pBIC, and which method performs better vary between scenarios.

\begin{figure}

  \centering
    \includegraphics[width=\textwidth]{1-loglik.png}
     \caption{\label{Fig4}The mean log likelihood on 1000 test datasets}
\end{figure}

\subsection{Adjusted rand index}
An alternative approach to assess the accuracy of the chosen shifts is
to compare the induced grouping of species based on the shifts. We
used adjusted rand index (ARI, \citealp*{hubert_arabie_1985}) between
the clustering of tips of the true model and the clustering of the
estimated shifts to evaluate the model performances. The ARI is
proportional to the number of pairs in agreement between two
clusterings. The ARI is scaled and centred so that identical
clusterings give an ARI of 1 and the expected ARI of two random
clusterings is 0.

Figure~\ref{Fig5} shows the ARI comparison of the different methods
with 3, 7, and 12 shifts. ARI shows a similar result to the prediction log
likelihood. When the signal sizes are small, the methods with BIC have
a better performance. When the signal sizes are large enough, the
methods with pBIC have a higher ARI score. PhylogeneticEM has low ARI
score when the signal sizes are small and good performances when the
signal sizes are in the middle. Based on true positive versus false
positive numbers, PhylogeneticEM has poor performance with 7 and 12
shifts, but its predictive log-likelihood and ARI are comparable to
other methods. This means that the shifts estimated by PhylogeneticEM
might not be the exact true shifts, but they give similar trait
predictions and trait clustering results.

\begin{figure}

  \centering
    \includegraphics[width=\textwidth]{1-ARI.png}
     \caption{\label{Fig5}ARI with 3, 7, 12 true shifts}
\end{figure}

\section{Effect of shift position and tree on model performance}
\label{sec4}

The previous simulations are all based on the same phylogenetic tree
and three different shift configurations depending on the number of
shifts. In this section, we perform simulations under a wider range of
shift positions and phylogenetic trees, to assess the effect of these
factors on the performance of the various methods.

\subsection{Shifts in different positions on the tree}
In this section, we study the influence of shift
position. Intuitively, different shift positions influence different
numbers of taxa and have different evolution time for the taxa so the
results might be different. Indeed the pBIC criterion is designed
specifically to account for the effect of shift position. We perform
simulations with only $1$ shift in different positions on the tree
(Figure~\ref{Fig6}). We perform simulations with $\beta=1,2,3,10$, $\alpha =1$
and $\sigma^2=2$.

\begin{figure}

  \centering
    \includegraphics[width=\textwidth]{2-position.png}
     \caption{\label{Fig6}Shifts in different positions of tree}
\end{figure}

Figure~\ref{Fig7} shows the true positive versus false positive curves
of different shift positions. When the coefficient size is very large,
all the methods can detect the true positive shift correctly,
regardless of its position. When the coefficient size is not large
enough, the shift near the root is the easiest one to detect. All the
methods have higher true positive values compared to shifts in other
positions. The result is in line with common sense --- shifts near
the root influence a large group of taxa and the evolution time after
the shift is longer, so the shift might have a larger influence on the
trait values, making it easier to detect. However, shifts near leaves
are easier to detect compared to shifts in the middle based on the
simulations for $\beta=2$ and $\beta=3$. And for $\beta=2$ and
$\beta=3$, ensemble LASSO+BIC performs better than $\ell$1ou+BIC at
detecting the shift near the root or the shift near the
leaves. PhylogeneticEM has a good performance for detecting the shift
near the root but has worse performance for detecting the shift near
the leaves and the shift in the middle.

\begin{figure}
  \centering
    \includegraphics[width=\textwidth]{2-compare.png}
     \caption{\label{Fig7}True positive versus false positive with different shift positions}
\end{figure}

Figure~\ref{Fig8} shows the predictive log-likelihood of the methods for
different shift positions. We only include 1 shift in these
simulations, and a conservative method usually has a higher prediction
likelihood when the number of shifts is small. Thus, for the shift near
the leaves and the shift in the middle, for predictive log-likelihood,
$\ell$1ou+pBIC $>$ ensemble LASSO+pBIC $>$ ensemble LASSO+BIC $>$
$\ell$1ou+BIC. However, for the simulations with the shift near the root,
ensemble LASSO shows its strength when the coefficient size is in the
middle of the range. Figure~\ref{Fig9} shows the ARI of methods with different
shift positions. Comparing the model performances on the scenarios
with different positions of shift by ARI, all the methods perform
better with the shift near the root, then shift near the middle,
and perform worst with the shift near the leaves. This may be because
of the way ARI is defined. For a shift on a leaf branch, the ARI of a
close surrogate shift is still relatively low, whereas, for shifts near
the root or the middle of the tree, close surrogate shifts have a
significantly higher ARI.

\begin{figure}
  \centering
    \includegraphics[width=\textwidth]{2-loglik.png}
     \caption{\label{Fig8}Log test likelihood with different shift positions}
\end{figure}

\begin{figure}
  \centering
    \includegraphics[width=\textwidth]{2-ARI.png}
     \caption{\label{Fig9}ARI with different shift positions}
\end{figure}

\subsection{Different types of tree}
In this section, we compare the model performances on shift detection
tasks on different types of phylogenetic trees. We mainly consider 4
types of tree: balanced tree, caterpillar tree, pure birth tree and
coalescent tree. We generate these 4 types of trees with 128 taxa and
254 edges. Figure~\ref{Fig10} shows the generated tree and simulated shifts for
each type of tree. In this simulation, there are 3 shifts on each
tree.

\begin{figure}
  \centering
    \includegraphics[width=\textwidth]{3-treetype.png}
     \caption{\label{Fig10}4 different types of tree}
\end{figure}

Figure~\ref{Fig11} shows the true positive versus false positive curves for
different types of trees. Interestingly, the shifts in the coalescent
tree are the easiest to detect when the coefficient size is small and
the most difficult to detect when the coefficient size is
large. Especially for ensemble LASSO, when $\beta=0.2$, ensemble
LASSO+BIC has a much higher true positive compared to other methods
and a lower false positive compared to $\ell$1ou+BIC. For other types of
tree, generally speaking when the coefficient size is in the middle of
the range, the shifts on the caterpillar tree are the easiest to
detect, and then the balanced tree, then the pure birth tree and finally
the coalescent tree. Figure~\ref{Fig12} shows the predictive log-likelihood. It
is obvious that ensemble methods perform very well on the coalescent
tree with small signal size and poorly with large signal
size. Figure~\ref{Fig13} shows the ARI with different types of trees. From the
ARI results, the order of difficulties of shift detection on
different types of trees is coalescent tree $>$ pure birth tree $>$
balanced tree $>$ caterpillar tree.

\begin{figure}
  \centering
    \includegraphics[width=\textwidth]{3-compare.png}
     \caption{\label{Fig11}True positive versus false positive with different types of trees}
\end{figure}
\begin{figure}
  \centering
    \includegraphics[width=\textwidth]{3-loglik.png}
     \caption{\label{Fig12}Average test log likelihood with different types of trees}
\end{figure}
\begin{figure}
  \centering
    \includegraphics[width=\textwidth]{3-ARI.png}
     \caption{\label{Fig13}ARI with different types of trees}
\end{figure}

\section{Robustness to violations of model assumptions}
\label{sec5}

The simulations above assume that the model assumptions in
Section~\ref{sec2} are met.  However, in reality, the real data often
violate the model assumptions. For example, the trait values might
contain measurement errors. Measurement errors enlarge the variance of
the data and make accurate detection more difficult. Another example
is that the phylogenetic tree is constructed based on sequence data,
thus can also have errors. These kinds of problems are hard to avoid in
real data analysis. It is, therefore, important to consider how the
methods perform when the model assumptions are not satisfied. In this
section, we discuss several violations of model assumptions. We also
study the effects of misestimation of parameters on model selection
performance. Because the $\ell$1ou and ensemble methods use a very
crude approach to estimate $\alpha$, it is important to examine the
question of how misestimation of $\alpha$ impacts results.
PhylogeneticEM does not suffer from this problem because it does not
have a preconditioning step.

\subsection{Measurement errors}
It is common for the measurement of traits to be subject to errors. These
errors may impact the shift detection methods, which assume that the
trait values are measured perfectly. In this section, we simulate
additive Gaussian measurement error $N(0,\sigma_e^2)$ for the trait value of each species. When $\sigma_e^2$ is larger, the size of the
measurement error is larger.

Figure~\ref{Fig14} shows the true positive versus false positive with
changing measurement errors. Figure~\ref{Fig15} shows the predictive
log-likelihood value with changing measurement errors in training
data. Because of the measurement errors in the training data, the true
shift model has a lower log-likelihood than the null model.  If
we use the training data with measurement error to estimate the
parameters of selected shifts, the parameters will be far from the
real values and thus introduce errors while calculating the predictive
log-likelihood. Since our purpose is to identify the true shifts,
predictive log-likelihood using training data with measurement error
to estimate the parameters is not an ideal way to assess the selected
shifts. Therefore, we also compare the prediction log-likelihood of the
shift detection methods using the training data with measurement error
to select shifts, but with parameters estimated from training data
without measurement error.
Figure~\ref{Fig16} shows the results of log likelihood with parameters
estimated from training data without measurement error.
Figure~\ref{Fig17} shows the ARI with measurement error. The plots show
that the measurement error will influence the accuracy of shift
detection. The loss of accuracy increases with the number of
shifts. The performance of all the methods worsens with measurement
error. When the signal is not so strong ($\beta=1$), even a relatively
small measurement error severely impacts performance. Interestingly,
in terms of ARI, when $\beta=10$, the performance of the methods using
BIC improves with increased measurement error. Looking at
the true and false positive rates, we see that the methods
consistently select the true variables, but with increased measurement
error, the false positive rate is reduced. However, the predictive
log-likelihood does decrease. This may be because even though failing
to select a true variable is very rare in this case, it has a huge
impact on the log-likelihood, because of the strong signal
strength. Thus the effect on predictive log-likelihood may be driven
by the small number of cases where a true variable is missed.

\begin{figure}
  \centering
    \includegraphics[width=\textwidth]{4-TPFP.png}
     \caption{\label{Fig14}True positive versus false positive with changing measurment error.}
\end{figure}

\begin{figure}
  \centering
    \includegraphics[width=\textwidth]{4-loglik.png}
     \caption{\label{Fig15}Average test log likelihood with parameters estimated from training data with measurement error}
\end{figure}
\begin{figure}
  \centering
    \includegraphics[width=\textwidth]{4-corrected_loglik.png}
     \caption{\label{Fig16}Average test log likelihood with parameters estimated from training data without measurement error}
\end{figure}
\begin{figure}
  \centering
    \includegraphics[width=\textwidth]{4-ARI.png}
     \caption{\label{Fig17}Distribution of ARI between selected shift configuration and true shift configuration with changing measurment error.}
\end{figure}

\subsection{Brownian-Motion model}
Another assumption that may be violated is that the evolution on each
branch follows an OU process. In this section, we use BM to simulate the trait values. This is a very simple simulation of this
violation since BM is a special case of the OU process
with $\alpha=0$. Other models should be considered in future studies.\

Based on the results of Figures~\ref{Fig18},~\ref{Fig19},
and~\ref{Fig20}, under the BM model, the results are similar compared
to the results under the OU model. There is no significant failure of
any method when applied to data generated from the BM model. The
strength of ensemble LASSO methods when there are 7 or 12 shifts
becomes more obvious. Ensemble LASSO+BIC has a lower false positive
rate compared to $\ell$1ou+BIC and similar or even higher true
positive rate. Ensemble LASSO+pBIC has a higher true positive rate
compared to $\ell$1ou+pBIC and similar or even lower false positive
rate.

\begin{figure}
  \centering
    \includegraphics[width=\textwidth]{5-TPFP.png}
     \caption{\label{Fig18}True positive versus false positive with BM model.}
\end{figure}
\begin{figure}

  \centering
    \includegraphics[width=\textwidth]{5-loglik.png}
     \caption{\label{Fig19}Average test log likelihood with BM model.}
\end{figure}
\begin{figure}
  \centering
    \includegraphics[width=\textwidth]{5-ARI.png}
     \caption{\label{Fig20}ARI with BM model. 3 true shifts simulations are shown in this figure.}
\end{figure}

\subsection{Incorrect tree}\label{MisspecifiedTree}
Another possible violation is that we use a wrong tree to do the shift
detection. There are two possible errors in the analyzed tree: wrong
topology or wrong branch lengths. In the case of an incorrect
topology, it is not always clear that there is a meaningful way to
define ``true shifts'' in a false tree. Therefore, we will focus on
the case with incorrectly specified branch lengths. We simulate data
from a tree with different branches and apply the methods to the
originally provided tree. We use a gamma distribution
($Gamma(\beta*\mathrm{original\ branch\ length},\beta)$) to randomly
generate each internal branch length. Thus, the mean of each branch
length is the original branch length, and the variance is larger for
smaller $\beta$. In order to generate ultrametric trees and keep the
total tree depth the same as the original tree, the external branches
are generated by the original tree depth minus the tree depth of the
starting node of each external branch. The internal branches are
generated with a depth first order. If the depth of one internal
branch is larger than the given tree depth, this branch length is
resampled. We generate 3 trees with $\beta = 30,10,5$ as shown in
Figure~\ref{Fig21}.

\begin{figure}

  \centering
    \includegraphics[width=\textwidth]{6-wrongtree.png}
     \caption{\label{Fig21}Regenerated tree with different beta}
\end{figure}

Firstly, we find that this violation of assumptions can cause
convergence problems while iterating the methods to estimate $\alpha$
in $\ell$1ou or the ensemble method. This is a problem we
observe for real data in Section~\ref{real_data}. Figure~\ref{Fig22}
shows the number of cases where the estimated alpha does not converge
in 10 iterations out of 200 simulations. The convergence problems seem
to particularly affect the ensemble method but are also present for
$\ell$1ou. For the non-converging simulations, we present results for
the best $\alpha$ value attempted (in terms of our model selection
criterion).

\begin{figure}

  \centering
    \includegraphics[width=\textwidth]{5-tree-2.png}
     \caption{\label{Fig22}Number of non-converging cases while analyzing on a misspecified tree }
\end{figure}

Figure~\ref{Fig23} shows the average number of true and false positives for
each misspecified tree. Figure~\ref{Fig24} shows the prediction
log-likelihood. Figure~\ref{Fig25} shows the ARI between the estimated and true
shift configurations.  We see that generally speaking, the difference
between the real tree and the analyzed tree will worsen the performance of
the methods by either lowering the true positive rate or increasing
the false positive rate. However, the methods are relatively robust to
this misspecification. PhyloEM appears to be most influenced by the
misspecification, becoming less conservative when the tree is
misspecified. This increases both the true positive rate and the false
positive rate. The predictive log-likelihood on test data is fairly
robust to the tree misspecification. However, the ARI decreases,
particularly for the cases with strong signals, as the tree becomes
more misspecified.

In some exceptional cases, the methods perform better when
the tree is misspecified. For example, for simulations with 7 or 12
true shifts, nearly all the methods got higher true positive rates
when the tree was most misspecified (rate parameter = 5) than for the
original tree.

\begin{figure}

  \centering
    \includegraphics[width=\textwidth]{6-TPFP.png}
     \caption{\label{Fig23}True positive versus false positive with applying methods on misspecified trees}
\end{figure}

\begin{figure}
  \centering
    \includegraphics[width=\textwidth]{6-loglik.png}
     \caption{\label{Fig24}Average test log likelihood with applying methods on misspecified trees}
\end{figure}

\begin{figure}
  \centering
    \includegraphics[width=\textwidth]{6-ARI.png}
     \caption{\label{Fig25}ARI with  methods applied on misspecified trees}
\end{figure}

\subsection{Misestimation of $\alpha$}
Recall from Section~\ref{sec2} that both $\ell$1ou and ensemble use a very rough
method to estimate $\alpha$. This could lead to the estimated $\alpha$
values being very bad. In this section, we investigate the extent to
which misestimation of $\alpha$ can impact the variable selection
results. In this simulation, we use $\alpha = 1$ to generate data and
use different $\hat\alpha$ $(10^{-4},10^{-3},...,10^2)$ in the methods
and compare the model performances.

Figure~\ref{Fig24} and Figure~\ref{Fig25} show the prediction log
likelihood and ARI with different estimated $\hat{\alpha}$. From the
plots, the performance of PhylogeneticEM is influenced most by
changes in the estimation of $\alpha$. Especially when the estimated
$\alpha$ is too large, the method performs poorly. Ensemble methods
and $\ell$1ou are more robust to misestimation of $\alpha$. Since PhlyoEM
uses maximum likelihood to estimate $\alpha$, which is expected to
produce more accurate estimates, robustness to misestimation is less
important than for $\ell$1ou and the ensemble method, which use a very
rough method to estimate $\alpha$.

\begin{figure}
  \centering
    \includegraphics[width=\textwidth]{7-loglik.png}
     \caption{\label{Fig26}Average test log likelihood with changing estimated alpha}
\end{figure}
\begin{figure}
  \centering
    \includegraphics[width=\textwidth]{7-ARI.png}
     \caption{\label{Fig27}ARI with changing estimated alpha}
\end{figure}

\section{Case study: Anolis Lizard data}\label{real_data}
The tree and trait data of Anolis lizards are provided by
\citet{mahler_ingram_revell_losos_2013}.  They applied PCA to 11
traits including body size, limb, tail length, and so on. They found
that the first four principal components explained 93\% of the
variation. The data are available in the \texttt{R} package
$\ell$1ou. We compare the results of the different variable selection
methods for the Anolis Lizard data.

\subsection{Results}

\begin{figure}

  \centering
    \includegraphics[width=\textwidth]{8-shifts.png}
     \caption{\label{Fig28}Shifts detected by different models for lizard data}
\end{figure}

Figure~\ref{Fig27} shows the shifts detected by applying
$\ell$1ou+pBIC, $\ell$1ou+BIC, ensemble LASSO+pBIC, ensemble
LASSO+BIC, and PhylogeneticEM on the first principal component of the
lizard traits. All the methods have similar results. $\ell$1ou+pBIC,
$\ell$1ou+BIC and ensemble LASSO+pBIC detected the same
shifts. Ensemble LASSO+BIC has slightly different results, only
differing for a small number of species. Both ensemble LASSO+BIC and
PhylogeneticEM detect one more shift near the leaf node that the other
three methods do not select. The estimated shift sizes range from
$-5.50$ to $-1.91$. By applying several methods to the same dataset,
we might say that the shifts which are selected by all the five
methods are highly likely to be true. While the shifts which are
selected by only one or two methods might need further investigation
and more biological evidence.

There are also convergence problems for the estimation of $\alpha$ for
this real dataset.  From Section~\ref{MisspecifiedTree}, this might
indicate that the tree is misspecified, or some other violation of the
model assumptions.

\subsection{Recommendations}
We suggest applying multiple methods to each data set and comparing
the results. Based on the simulation results, different methods have
different strengths and we cannot say that any method outperforms the
others in every situation. For example, the methods with pBIC are the
most conservative: they perform best with large signal sizes. They can
detect the true shifts without introducing false positive
shifts. However, they cannot give reasonable results when the signal
sizes are small. The ensemble method with BIC can better capture the
shifts near the leaves. PhylogeneticEM is even more conservative with
small signal sizes and falls between methods with pBIC and with BIC,
with large signal sizes. It is hard to tell which method and criterion
is the most suitable to use in a specific task. By comparing the
results of different methods, we can get the confidence level of the
selected shifts. For example for the Anolis lizard data, the shifts
which are selected by all the five methods are highly likely to be
true. \citet{khabbazian_kriebel_rohe_ane_2016} use bootstrap support
to evaluate how likely the selected shifts are true. However, the
bootstrap support can be influenced by biases in a particular
method. By combing the results of several different methods, we can
assess the confidence of particular shifts in a way that is unlikely
to be influenced by the bias of any particular method.

\section{Conclusion}
\label{sec7}
In this article, we compared the performances of several shift
detection methods --- $\ell$1ou, PhylogeneticEM, ensemble method --- for
trait evolution models. To understand the strength, weaknesses, and
restrictions of different methods, we compared the performances over a
large range of scenarios. We used three different measurements to
compare the results, true positive versus false positive curve,
predictive log-likelihood and adjusted rand index.

From the simulation results, when the coefficients are very small,
PhylogeneticEM, $\ell$1ou+pBIC and ensemble+pBIC are very strict and
tend to select nearly no shifts. In these scenarios, ensemble+BIC and
$\ell$1ou+BIC perform better at detecting the small magnitude
shifts. However when the coefficients are large, nearly all the
methods can detect the true shifts, but $\ell$1ou+BIC and ensemble+BIC
include more false positive shifts. The performances of methods are
highly dependent on the criterion. A better criterion might help the
methods to give very good results with varying signal sizes. Further
research about appropriate model selection criteria for shift detection
might be an interesting topic for future studies.

Furthermore, we compared the model performances on different shift
positions in trees and different types of trees. From the results, the
shifts near the leaves are the most difficult to detect and the shifts
near the root are the easiest to detect. The shifts on the coalescent
tree are the easiest to detect when the coefficient is small and the
most difficult to detect when the coefficient is large.

We also conducted simulations in several scenarios where the model
assumptions do not hold. We studied training data with measurement
error; misspecified phylogenetic trees; a non-OU model; and
misestimation of the parameter $\alpha$. From the simulation results,
measurement error and a misspecified phylogenetic tree cause shift detection
more difficult and all the methods perform worse in these
cases. $\ell$1ou and the ensemble method are robust to misestimation
of $\alpha$. The simulations under a BM model in this article do not
cause much difficulty. However, further research into different model
misspecifications is still necessary.

In the Anolis Lizard real data case study, all five methods estimate
similar shift configurations. There are only a few differences in the
shifts near the leaves. The shifts which are selected by all the five
methods are highly likely to be true, while the shifts which are
selected by only one or two methods might need further
investigation. Interestingly, we observe convergence problems for the
estimation of $\alpha$ for this real data with $\ell$1ou and the ensemble
method. In our simulations, estimation on training data with
a misspecified tree often led to this convergence problem, which
suggests that the Anolis Lizard tree may be misestimated.

\section{Data accessibility}
The tree and trait data of Anolis lizards are provided by \cite{mahler_ingram_revell_losos_2013}.  Our \texttt{R} package is available at \url{https://github.com/WenshaZ/ELPASO}.

\section*{Acknowledgement}
LSTH was supported by the Canada Research Chairs program, the NSERC Discovery Grant RGPIN-2018-05447, and the NSERC Discovery Launch Supplement.
TK was supported by the NSERC Discovery Grant RGPIN/4945-2014.

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text:
SSSHL Department of Humanities and Social Sciences
School of Social Sciences, Humanities & Law
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text:
Q: How to merge the changes of a Work Item to branches other than the source branch? My question is similar to this older question To merge from the current branch into the other branch, except that I would like to know whether this can be done directly with TFS 2017 / 2019.
Assume we have a master branch that contains all the features of our software. For each one of our few clients, we have a separate client branch that contains only the features that the client wants. When we work on a new feature, we create a Work Item along with a feature branch from the master branch. When feature is completed, we would like to merge (via pull request) the feature branch to the master, and every client branch where this feature is to be deployed. Ideally, the pull request on all branches should fail, if the build on any branch fails. Also, if later on, another client that does not have that feature wants it, we would like to merge it into their client branch.
Can this be done with TFS 2017 (or TFS 2019)?
Thanks.

A:
we would like to merge (via pull request) the feature branch to the master, and every client branch where this feature is to be deployed.

Merge the feature branch to the master should be simple, you can just merge it via pull request.
To deploy this feature to every client branch may be cumbersome, since client branch that contains only the features that the client wants not all features on master.
You can try to use cherry-pick in Visual Studio or git command line to copy the changes to the client branch.
If your client branch have branch policies, you could create a new branch based on the client branch without branch policies, then merge the feature to the new branch, finally, to create a pull request to merge the new create branch to the client branch.
If I don't understand your question correctly, please update your question with more info. The title of your question makes me a bit confused.
Hope this helps.

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text:
Tagged: Asia
Slingshot Philippines 2016 pushes for the development of the innovation economy
Slingshot Philippines 2016 startup exhibits mounted at the PTTC featured funders and accelerators; startup/social enterprises, education services, and co-working spaces/incubators.
The Department of Trade and Industry (DTI) staged just recently Slingshot Philippines 2016, the National Summit on Startups and Innovation, at the Philippine Trade Training Center (PTTC) in Pasay City, kicking off a two-day national summit on startups and innovation with the theme, "Accelerating the Innovation Economy."
The two-day summit provided an opportunity to gather under one roof young minds with big ideas, innovators in constant pursuit of new things to benefit their community, supportive investors who fund start-up ideas and the policymakers whose role is to provide an enabling business environment.
Breakout sessions are also conducted regarding topics on mentoring and coaching, programs for startups and innovators, and business development strategies for startups.
The event kicked off national consultations for the crafting of policies and programs to develop a new breed of innovative enterprises, covering the fields of agriculture, biotechnology, electronics, and digital startups, among others.
Around 50 speakers and panelists spoke on issues concerning funding, the current status and the future of Philippine startups, and strategies towards a Philippine innovation economy before 577 conference delegates who gathered at Slingshot Philippines 2016 to actively take part in a government-initiated platform for public dialogue and partnership that will help boost the development of Philippine startups for a robust innovation economy.
Trade Secretary Adrian Cristobal, Jr., Trade Undersecretary Nora Terrado, and Senator Paolo "Bam" Aquino share their thoughts and policies for the future of the Philippine innovation economy.
Slingshot Philippines 2016 conference at PTTC. (Source: Twitter account of Seedstars)
"Changing the ecosystem and putting in place policies responsive to the needs of the innovativion economy will not happen overnight. However, close collaboration of the government and key players will enable the Philippines to foster the desired new breed of business more quickly and efficiently. We want the next big tech startup to come from the Philippines ," said Trade Secretary Adrian Cristobal, Jr.
Secretary Cristobal, a steadfast advocate of MSMEs as an integral part of the country's pursuit towards regional economic integration and the attainment of equitable and inclusive growth, pointed out the government's main role in the upbringing of the innovation economy. DTI has started implementing programs such as Slingshot to help overcome the hindrances that constrain the growth of MSMEs as well as provide startups and innovative businesses an environment that will nurture their capabilities.
US Ambassador Philip Goldberg states the programs implemented by the US government that benefits the startup innovation economy as part of the Philippines' forged alliance with the US.
Sherwood Neiss, principal at Crowd Venture Capital Fund and one of the key speakers at Slingshot Philippines 2016, talks about effective business financing and crowdsourcing.
Panel discussion with (from left) Moderator RJ Ledesma, Elaine de Velez, CEO of Frontlearners; Ronald Magleo, founder and CEO of MarketaPH; Aurora Mendoza, founder and CEO of PinoyTravel; Mikko Perez, founder and CEO of Ayannah; and Dexter Ligot Gordon, co-founder and COO of Kalibrr.
In 2015, the Philippine government and startup community unveiled a roadmap which will serve as a framework in building a thriving ecosystem in the next few years. From a hundred, the country targets to have 500 startups with total valuation of USD2 B by 2020. This roadmap also serves as the basis for Senator Paolo "Bam" Aquino IV's Startup Bill which is set to be filed at the 17th Congress in July. This proposed policy will exempt startup entrepreneurs from paying taxes in their first two years, enabling an inclusive growth to their budding business.
Karla Legaspi of the Department of Science and Technology-Information and Communications Technology (DOST-ICT) talks about programs for startups during a Breakout Session at Slingshot Philippines 2016.
For three years in the senate, Senator Aquino has been the forerunner of six startup-friendly policies that support the innovation community. Among these are the Go Negosyo Act (RA 10644) which initiates the establishment of 'Negosyo Centers' that create linkages to bigger markets and financing as well as provide jobs and livelihood to people, the Microfinance NGOs Act (RA 10693) which supports the groups that guide and empower micro entreprenuers, and the Youth Entrepreneurship & Financial Literacy Act (RA 10679) which builds a culture of entrepreneurship as an alternative solution to unemployment and underemployment.
"Why (support) MSMEs? Because we must. Our MSME sector is at the forefront of pushing the growth of the Philippines," said Senator Paolo "Bam" Aquino IV, chair of the Senate Committee on Trade, Commerce and Entrepreneurship.
Former Trade Secretary Gregory Domingo (left) discusses the Arthrologic Knee Replacement System, an innovation by Cebuano orthopedic surgeon Dr. Rene Catan.
For her part, Trade Undersecretary Nora Terrado of the Industry Promotions Group (IPG) commended the local economy's unyielding growth despite the many challenges the country faces with the global economic slowdown. "We have gained momentum, but we can do more. We realized from our dialogues with the private sector that our country can benefit from a far-reaching and scalable innovation within a well-planned, well-organized government supported frameworks," said Terrado.
Over the past few years, the Philippine economy has shown a promising ground for the startup community. In 2015, the country jumped 83rd in the Global Innovation Index, 17 notches higher from its previous rank in 2014. Bloomberg Businessweek also revealed that the Philippines' ranked sixth among the top countries where talents are being sourced.
Featured in the Slingshot Philippines 2016 exhibit mounted at the PTTC is the electric quadcycle known as "eQUAD," one of the first locally designed, manufactured, and mass produced electric vehicles by the Philippine Utility Vehicle (PhUV) Incorporated.
The current status of the Philippine economy will serve as a viable launching pad for local startups and innovators to make it in the global market. The country is the second fastest-growing economy in Asia next to China and it currently flourishes from a positive business climate, a population of young, English-proficient speakers; and the stable remittances from overseas Filipinos.
Slingshot Philippines 2016 was the direct offshoot of Slingshot Manila 2015, the official startup and innovation event for the Philippines' hosting of the Asia Pacific Economic Cooperation (APEC) organized by the DTI in partnership with the Department of Science and Technology (DOST) and other key industry stakeholders. It was held in conjunction with the recently concluded 63rd edition of Manila FAME and Design Week Philippines (DWP).
Slingshot Philippines 2016 Project Team from the Department of Trade and Industry (DTI), Center for International Trade Expositions and Missions (CITEM), the Foreign Trade Service Corps (FTSC), and the Office of the Undersecretary of the Industry Promotion Group (OUIPG).
Written by CITEM Leave a comment Posted in Design Week Philippines, Manila FAME April 2016, Slingshot Philippines 2016	Tagged with 63rd edition of Manila FAME, Accelerating the Innovation Economy, Adrian Cristobal, Jr., APEC, Arthrologic Knee Replacement System, Asia, Asia Pacific Economic Cooperation, Bam Aquino, Bloomberg Businessweek, Center for International Trade Expositions and Missions, China, citem, Crowd Venture Capital Fund, Department of Science and Technology, Department of Science and Technology-Information and Communications Technology, Department of Trade and Industry, design week philippines, DOST, DOST-ICT, dti, DWP, electric quadcycle, entrepreneurs, eQUAD, Foreign Trade Service Corps, FTSC, Global Innovation Index, Go Negosyo Act, Gregory Domingo, Industry Promotions Group, Karla Legaspi, Manila FAME, Manila FAME April 2016, Microfinance NGOs Act, MSME, National Summit on Startups and Innovation, Negosyo Centers, Nora Terrado, Office of the Undersecretary of the Industry Promotion Group, OUIPG, Paolo "Bam" Aquino IV, Philip Goldberg, Philippine economy, Philippine innovation economy, Philippine startups, Philippine Trade Training Center, Philippine Utility Vehicle Incorporated, philippines, PhUV, Inc., PTTC, RA 10644, RA 10679, RA 10693, Rene Catan, Senate Committee on Trade, Commerce and Entrepreneurship, Sherwood Neiss, Slingshot Manila 2015, Slingshot Philippines 2016, Startup Bill, startups, Youth Entrepreneurship & Financial Literacy Act
Local craftsmen take center stage in international exhibition Manila FAME
Artisans from Albay, Cagayan Valley, Laguna, and Davao are ready to meet big retail firms at the Philippines' premier trade fair in April
Handcrafted furniture, home décor, and accessories from different Philippine regions get a design update through the Partner Province Program of CITEM in collaboration with internationally acclaimed designer PJ Arañador.
With support from PJ Arañador, lifestyle designer, local manufacturers from Cagayan Valley, Laguna, Albay, and Davao are getting ready to exhibit their finest furniture, home décor, and accessories at The Artisans Village in the boutique edition of Manila FAME this April.
The Artisans Village is a partner province program that aims to help regular and new Manila FAME exhibitors market their products in the global export scene. It is organized by the Center for International Trade Expositions and Missions (CITEM), the export promotions arm of the Department of Trade and Industry (DTI), in partnership with LGUs (local government units) and different DTI regional and provincial offices that share CITEM's aim to promote island crafts and celebrate Filipino craftsmanship.
The new products will be showcased at The Artisans Village, one of the highlights of the Manila FAME April 2016 Boutique Edition, to be held on April 21-24, 2016, at the World Trade Center Metro Manila, Pasay City.
Arañador leads The Artisans Village as the program's creative director. He is the owner of the PJ Aranador Design Studio One; the Nautilus Boutique in Boracay; WAWA, Iloilo's First Heritage Restaurant; and Escuela de Artesanias de las Filipinas, a crafts school. His creations are sold across Asia, Africa, North and South America, Europe, Australia, the Caribbean, and Middle East. Arañador is an international environmental design activist and a consultant for Go Green Philippines, an environmental program that aims to revitalize the Philippine ecosystem and uphold the country's fashion accessories industry. He is a leading social design entrepreneur in the Philippines, helping the poor in many rural communities through crafts production. He also advocates trade fair, ethical livelihood, preservation and advancement of indigenous materials, and innovative use of traditional techniques with green technology. He has been an exhibit curator for fairs in Colombia, Peru, and India.
PJ Arañador with Alex Cueva and Arlene Cueva of Disenio De Craftico in Albay
Arañador is conducting visits to Cagayan Valley, Laguna, Albay, and Davao to hold product development and one-on-one prototype checking sessions, with the assistance of seasoned design and product development specialist Imelda Datul, and rising designer Rachelle Dagñalan.
In addition, Arañador is coaching the exhibitors on the ins and outs of the export business, covering topics such as identifying the target market; selecting the right colors for products; determining the proper pricing and costing; marketing the products offline and online; executing product displays; and handling operations, income, and billing. As a mentor, Arañador not only focuses on the technical side of business, but reminds the exhibitors to be vigilant against frauds as well.
With 25 years of professional experience in designing and exposure to products and designs around the world, Arañador believes that one of the factors that sets Philippine products apart from those of other countries is the resourcefulness or ingenuity of the Filipinos. "While the technology may not be as advanced, with so much less, we become very creative. And I think that's a good position because crafts are after all made by hand. That's when the soul of the products reflects more," he said.
Manila FAME, the country's premier design and lifestyle trade show, will be held on April 21-24 at the World Trade Center Metro Manila in Pasay City. For more information on The Artisans Village and Manila FAME, please log on to www.manilafame.com.
Written by CITEM Leave a comment Posted in 63rd Manila FAME, Manila FAME, Manila FAME April 2016, The Artisans Village	Tagged with 63rd Manila FAME, accesories, Africa, albay, artisan, Asia, Australia, Boracay, Boutique Edition, cagayan valley, Caribbean, Center for International Trade Expositions and Missions, citem, Colombia, davao, Department of Trade and Industry, dti, Escuela de Artesanias de las Filipinas, Europe, Filipino Craftsmanship, furniture, Go Green Philippine, handcrafted, Home Decor, Iloilo's First Heritage Restaurant, Imelda Datul, India, laguna, LGU, local government units, Manila FAME, Manila FAME April 2016, Middle East, Nautilus Boutique, North America, Partner Province Program, Pasay City, Peru, philippines, PJ Arañador, PJ Aranador Design Studio One, Rachelle Dagñalan, South America, The Artisans Village, WAWA, World Trade Center Metro Manila
Asian firms at Mla FAME, Oct. 15-18
The country's premier international lifestyle trade show opens on Thursday (Oct. 15) with highly visible participation of exporters from India, Indonesia, Hong Kong, Malaysia and Taiwan, highlighting the event's global character and the Philippines' emerging image as a design creativity hub.
"The exhibits at this forthcoming October edition 2015 of Manila FAME will bring to the fore both the similarities and differences among the lifestyle export products of Asia's constituent economies," said Executive Director Rosvi C. Gaetos of the Center for International Trade Expositions and Missions (CITEM), the event's organizer and the export promotions arm of the Department of Trade and Industry (DTI).
"It is here (Manila FAME) where we may see Asia's collective strength as a regional trade bloc, and assess the actual global competitiveness of Philippine lifestyle exports vis-à-vis those of our fellow Asians, especially in terms of universal appeal and market acceptance," Gaetos said of the estimated 5,000 international trade visitors and buyers expected to swarm the event at the SMX Convention Center, Mall of Asia, during its 4-day run up to Oct. 18.
The foreign exporters will have their products exhibited at the Manila FAME's International Hall, where their fashion items, furniture and furnishings, home décor, gifts and premium will be arrayed with those of the Philippines in other special settings, all occupying the entire 17,156-sqm exhibit space of the SMX.
The special settings for the Philippine export exhibits are the Artisan Villages for artisanal crafts, The Manila Gusto Gallery: In Pursuit of Pleasure for a rare blend of the classic and the modern in houseware design, the Light & Shadow for the industry's new lighting interpretations for 2015 as the International Year of Light and Light-based Technologies as proclaimed by the United Nations, the Red Box program for the design collections of today's generation of budding professionals and manufacturers, the Manila Wear for tropical fashion expressions, the NeoTextiles Philippines for the country's commercially viable and globally competitive fabrics, and the OTOP (one town, one product) Marketplace for regional entrepreneurial development as manifested by community-based but export-quality products.
All in all, over 400 exhibitors from the top ranks of the country's lifestyle export industry have been tapped by CITEM for the 62nd Manila FAME, which is seen to yield spot sales of up to P73 million for domestic consumption and $26.5 million for foreign, a big leap from the year-ago level of P45.13 million domestic, and $13.38 million export.
The previous Manila FAME October edition drew more than 300 exhibitors and over 3,000 foreign and local trade visitors and buyers as against this year's target of 450 and 5,000.
To address them at the welcome reception as guest of honor is DTI Secretary Gregory Domingo, who will open the show and present the winners of the Katha Awards, a special distinction conferred on exporters and designers for exceptional product quality and design in various export categories.
Invited to join Domingo at the ceremonies, according to Gaetos, are Governors Joey Salceda of Albay and Ramil Hernandez of Laguna to represent the local government units in the Artisan Villages exhibition, and DTI Undersecretary Zenaida Maglaya, Agriculture Undersecretary Emerson Palad, Philippine Fiber Industry Development Authority Officer-in-Charge Dr. Clarito Barron, and DTI regional directors in Cagayan Valley, Southern Tagalog, Bicol and Davao, and provincial directors in Laguna and Albay.
Assisting CITEM in organizing Manila FAME are business support and trade promotion organizations such as the Foreign Trade Service Corps, DTI regional and provincial offices, Philippine embassies, local government units, Foreign Buyers Association of the Philippines, Bureau of Domestic Trade, Export Marketing Bureau, and the Department of Agriculture – Agribusiness and Marketing Assistance Service.
Manila FAME is the Philippines' biggest international trade fair for exports from various industry sectors and communities. It is one of the longest-running trade shows in Asia Pacific and the only event approved by the Union des Foires Internationales (UFI), the Global Association of the Exhibition Industry.
For more information, please log on to www.manilafame.com.
Written by CITEM Leave a comment Posted in 62nd Manila FAME	Tagged with Asia, Asia's Design and Lifestyle Event, citem, design, Design and Lifestyle Event, dti, fashion, foreign exhibitors, furniture, Gifts, Home Decor, International Hall, Katha Awards, lifestyle, Manila, Manila FAME, ManilaFAME, philippines
Manila FAME forges breakthrough partnership with prominent design brands Café Elle Deco + DEDON
Considered as a breakthrough in its pursuit to strengthen the Philippines' positioning as Asia's design trendsetter, Manila FAME partners with prominent international design brands Elle Decoration and DEDON to bring an exciting new concept of a branded Café dubbed as "Café Elle Deco + DEDON."
DEDON is a worldwide outdoor furniture brand that constantly raises the bar for quality and design. A brainchild of former German football player Bobby Dekeyser, DEDON was the first furniture company to weave synthetic fiber into beautiful luxury outdoor products. Much like Manila FAME, DEDON has always been an embodiment of top quality and design excellence. Today, DEDON constantly reinvents their products and ships to more than 80 countries around the world.
Synonymous with the words quality, style and design, the Elle Decoration brand has always been a staple in the international design scene. Elle Decoration has been featuring world-class designers, design-forward interiors, and must-have furnishings for over two decades. Recently, the country's leading magazine publisher Summit Media brought this international design brand to the Philippines.
This milestone of a partnership brings together a tripartite of design brands that fortifies Manila FAME's mission to bring to the fore innovative and design-forward products that appeal to the international market. The Manila FAME + Elle Decoration + DEDON partnership expresses the country's mission to showcase exceptional Filipino craftsmanship as well as highlight the country's positioning as Design Philippines.
Curated by talented Philippine designer Jinggoy Buensuceso, Café Elle Deco + DEDON, together with concessionaire Coffee Bean and Tea Leaf, will co-host the opening ceremony of Manila FAME and the Katha Awards on 14 March 2013.
Organized by the Center for International Trade Expositions and Missions (CITEM), the export marketing arm of the Philippine Department of Trade and Industry (DTI), Manila FAME is all geared up to bring new and exciting show features in its upcoming edition.
See this groundbreaking partnership come to life! Visit Manila FAME on 14-17 March 2013 at the SMX Convention Center, Metro Manila, Philippines.
Written by CITEM 1 Comment Posted in Manila FAME	Tagged with 2013, Asia, Asia's Design and Lifestyle Event, Bayern Munich, Bobby Dekeyser, Cafe Elle Deco + DEDON, Center for International Trade Expositions and Missions, citem, Coffee Bean, DEDON, Department of Trade and Industry, design, dti, Elle Deco, Elle Decoration, Football, German, Germany, Katha Awards, Manila FAME, March, March 2013, Partnership, Quality, smx convention center, Style, Tea Leaf


text:
' Copyright (c) Microsoft.  All Rights Reserved.  Licensed under the Apache License, Version 2.0.  See License.txt in the project root for license information.

Imports System.Collections.Immutable
Imports System.Threading
Imports System.Threading.Tasks
Imports Microsoft.CodeAnalysis.CodeGeneration
Imports Microsoft.CodeAnalysis.Editing
Imports Microsoft.CodeAnalysis.VisualBasic.Syntax

Namespace Microsoft.CodeAnalysis.Editor.VisualBasic.NavigationBar
    Friend Class GenerateEventHandlerItem
        Inherits AbstractGenerateCodeItem

        Private ReadOnly _containerName As String
        Private ReadOnly _eventSymbolKey As SymbolKey
        Private ReadOnly _destinationTypeSymbolKey As SymbolKey

        Public Sub New(eventName As String, glyph As Glyph, containerName As String, eventSymbolKey As SymbolKey, destinationTypeSymbolKey As SymbolKey)
            MyBase.New(eventName, glyph)

            _containerName = containerName
            _eventSymbolKey = eventSymbolKey
            _destinationTypeSymbolKey = destinationTypeSymbolKey
        End Sub

        Protected Overrides Async Function GetGeneratedDocumentCoreAsync(document As Document, codeGenerationOptions As CodeGenerationOptions, cancellationToken As CancellationToken) As Task(Of Document)
            Dim compilation = Await document.Project.GetCompilationAsync(cancellationToken).ConfigureAwait(False)
            Dim eventSymbol = TryCast(_eventSymbolKey.Resolve(compilation).GetAnySymbol(), IEventSymbol)
            Dim destinationType = TryCast(_destinationTypeSymbolKey.Resolve(compilation).GetAnySymbol(), INamedTypeSymbol)

            If eventSymbol Is Nothing OrElse destinationType Is Nothing Then
                Return Nothing
            End If

            Dim delegateInvokeMethod = DirectCast(eventSymbol.Type, INamedTypeSymbol).DelegateInvokeMethod

            If delegateInvokeMethod Is Nothing Then
                Return Nothing
            End If

            Dim containerSyntax As ExpressionSyntax
            Dim methodName As String

            If _containerName IsNot Nothing Then
                containerSyntax = SyntaxFactory.IdentifierName(_containerName)
                methodName = _containerName + "_" + eventSymbol.Name
            Else
                containerSyntax = SyntaxFactory.KeywordEventContainer(SyntaxFactory.Token(SyntaxKind.MeKeyword))
                methodName = destinationType.Name + "_" + eventSymbol.Name
            End If

            Dim handlesSyntax = SyntaxFactory.SimpleMemberAccessExpression(containerSyntax, SyntaxFactory.Token(SyntaxKind.DotToken), eventSymbol.Name.ToIdentifierName())

            Dim methodSymbol = CodeGenerationSymbolFactory.CreateMethodSymbol(
                attributes:=Nothing,
                accessibility:=Accessibility.Private,
                modifiers:=New DeclarationModifiers(),
                returnType:=delegateInvokeMethod.ReturnType,
                refKind:=delegateInvokeMethod.RefKind,
                explicitInterfaceImplementations:=Nothing,
                name:=methodName,
                typeParameters:=Nothing,
                parameters:=delegateInvokeMethod.Parameters,
                handlesExpressions:=ImmutableArray.Create(Of SyntaxNode)(handlesSyntax))
            methodSymbol = GeneratedSymbolAnnotation.AddAnnotationToSymbol(methodSymbol)

            Return Await CodeGenerator.AddMethodDeclarationAsync(document.Project.Solution,
                                                                 destinationType,
                                                                 methodSymbol,
                                                                 codeGenerationOptions,
                                                                 cancellationToken).ConfigureAwait(False)
        End Function
    End Class
End Namespace

redpajama_set_name: RedPajamaGithub


Mayor C. Kim Bracey will hold The Diverse & Minority Business Forum: A Business Diversity and Inclusion Event on Monday October 30, 2017, 9:00 a.m. – 4:00 p.m., located at York College Graham School of Business, 441 Country Club Road, York, PA 17403, sponsored by Governor Tom Wolf, State Representative Carol Hill – Evans, City of York, York College of Pennsylvania, York City School District, and York County Economic Alliance. There is no cost to attend the event.
The event will assist in learning how to do business with the City of York, Commonwealth of Pennsylvania, York College of Pennsylvania, and York City School District.


The internet has certainly made making payments much easier over the past few years. While many of us still use the banks whenever it comes to payments and transfers, there are easier ways online. These ways of sending and receiving money online have greatly benefited businesses throughout the world, and are being used more and more often by people themselves. Of course, naturally, when it comes to sending and receiving money, people can be rightfully skeptical. Everybody needs a solid service which can provide all the necessary functions without putting anybody's finances in jeopardy. So, where does one go to when this is the case? One particular website that has welcomed millions of people is PayPal. We have all heard of PayPal, but not all of us may have used it. So, is it really worth using?
That's what we at Filmbiz are here to investigate today. PayPal is without a doubt one of the most popular eCommerce websites in the world (and in fact one of the most popular websites in the world in general). So how has it attracted so much attention and what makes it so great, it is great at all?
PayPal began back in 1998 under the name Confinity. At its head was an unknown man by the name of Elon Musk. Today, Musk is just behind main man Bill Gates in terms of net worth. As a multi-billionaire, Musk is constantly looking out for the best ways in which to improve his company. In fact, PayPal brings in just under half of eBay's total earnings. All this is due to just how popular the service is, and it may very well be the most popular and successful online banking service out there on the net. The overall premise of PayPal is very straight forward. With a PayPal account, you can receive and send money within quick time. Such activity within your standard bank will take on average a few days. With PayPal, however, these actions take only a matter of minutes to complete.
Sounds good? Well, it you don't already use PayPal but you are interested and could potentially use it in the future, the following review will outline all the features and the important information you need to know when using it. Hopefully, PayPal can benefit you, or alternatively, warn you of the damage it could potentially do to your finances. Either way, let our review help you to decide.
Do I Have To Sign Up In Order To Use This Service?
Yes, you must sign up in order to use the full services. Signing up to PayPal can seem like a long process, but once everything is set up, you can begin reaping the benefits. Signing up to the service is free, and there are a few steps which you will need to take. You will need to provide personal information, from your full name to your address, and also enter your card and bank details. Don't be suspicious about the request for card and bank details. This is asked from you in order to set up future payments. By giving your bank and card details, you can then fully receive and send money, without your bank being suspicious of any activity.
As mentioned above, it is free to sign up to this service. As for fees for using the PayPal service, there are none. PayPal is completely free to use, which is probably why it attracts so many users.
How Do I Transfer Money Into My Bank Account and How Long Will It Take?
When you receive money from an employer, friend, etc., you will see the money in your account. This transfer may take a few minutes. Once the sum appears in your account, you can then select 'Withdraw money'. Please make sure that you have already connected your bank account to your PayPal account. If your account is connected, this action cannot be completed. Following the selection of 'Withdraw money', you will then choose which bank account you want to withdraw the money into. Select the bank account, and then enter the amount you want to transfer. Once complete, there's nothing more to do than to complete the action.
Once complete, it may take as long as a few minutes to a few hours for the transaction to go through. Depending on your bank and other circumstances, some accounts may take longer than others to complete withdrawal transactions. Personally, my transactions take only around 20 minutes to go through.
Are There Any Negative Aspects Regarding PayPal?
Please be aware that PayPal take a small sum of your transactions for service fees. The for each transaction is 2.9% plus $0.30 USD of the amount you receive. This is non negotiable and cannot be avoided.
Another thing to be aware of is the annual earnings limits. You cannot receive more than 2,500 euro within a year – you must modify or lift these limits if it should occur. To lift these mandatory limits, you must select 'view your account limits' and then proceed to eliminate these limits. PayPal will ask for a range of identification in order to complete this action.
Can I Transfer Money and How Long Will It Take?
You can 'Add money', if your bank account is linked, and then send money by entering the recipient's email address. You can do this by selecting 'pay or send money' at the top of the page. Depending on your bank and your recipient's circumstances, it may take longer. Normally, PayPal transactions are quick and shouldn't take longer than a few hours.
PayPal is popularly used mainly due to how secure it is. You will find when using it that this service carries out many safety procedures, requesting many details and identification documents. So, in sum, yes, it is very safe.
Besides the fees and the limits (which can be lifted), PayPal is ultimately both very safe and efficient, as well as free, which gives you no real reason not to try it out.


text:
Home News Social care cases to be audited after user-group claims council breached Care...
Social care cases to be audited after user-group claims council breached Care Act
Businessman reading documents with magnifying glass concept for analyzing a finance agreement or legal contract
Norfolk Council requested the audit. It will be carried out by SCIE and funded by the Local Government Association
Adult services at Norfolk Council will be externally audited after a local user-group reported the organisation to regulators for breaching the Care Act.
The council requested the audit. It will be carried out by the Social Care Institute for Excellence (SCIE) and funded by the Local Government Association.
The move follows a complaint made by a local user-group in February this year.
Equal Lives reported the council to the Care Quality Commission for 'disregarding' its duties under the Care Act.
The user-group claimed eligibility thresholds had been raised and care package reviews were being used to reduce, or completely withdraw, support for people regardless of need.
It also said the decision to remove 'wellbeing' payments for personal budget holders in 2014 had had a "devastating impact" on service users' lives.
The council has strongly disputed any claims that it breached its statutory duties.
'Care Act implementation'
Equal Lives escalated the complaint last month by sending a letter to health secretary Jeremy Hunt, which urged him to order the CQC to investigate Norfolk's adult social care department.
Norfolk's executive director for adult social services Harold Bodmer told Community Care that the council still maintained the 'systemic nature' of the complaint was unjustified, but it was responding to any issues.
"We have engaged SCIE to do a piece of work with us – of course looking at the complaint that was made but also helping us to think about the implementation of the Care Act," he said.
"We hope we will be able to use this as a real development and learning opportunity to improve practice in the context of the financial position that we are in."
SCIE will review the council's policies and procedures, meet with key stakeholders, talk to service users and social workers, and audit some cases.
Minutes from a meeting of the council's adult social care committee last month said SCIE would also assist on further development work on casework.
'Inspection with teeth'
Mark Harrison, chief executive of Equal Lives, said: "I would welcome the SCIE review if it helps implement the Care Act and make personalisation meaningful. However, given the culture within Norfolk and the level of resources, I'm not sure that can be achieved. The council is operating a social care system based on available resources, not on the legislation.
"I want to be very clear that we are still pressing the Department of Health and the CQC to investigate failings in Norfolk because, based on the evidence we've presented, we believe that is necessary. The decision the council made in 2014 to cut 'wellbeing' payments, and the funding decisions it has taken since then, have taken more money out of adult social care.
"What we want is a truly independent inspection that has teeth, which can tell Norfolk if it has breached the Care Act, where it has breach it and what resources it can put in.
"They are trying to pretend they can do this on a wing and prayer, but the reality is people's social care budgets are being devastated."
The adult social care committee papers also stated that a member of council staff could be based at Equal Lives for a proportion of their working week, to improve direct communication.
Mark Harrison said he had discussed this with Norfolk at a meeting in February and "in principle, it was a good idea", but he had not met with directors since to confirm the terms of reference.
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text:
Juan Alfonso Faroppa Fontana (Montevideo, 19 de diciembre de 1959) es un abogado y político uruguayo, perteneciente al Frente Amplio.

Biografía
Faroppa estudió leyes en la Universidad de la República, egresando como abogado en 1986. Posteriormente realizó cursos sobre Derechos Humanos en la Universidad de Utrech-Universidad de Tucumán y en el Instituto Interamericano de Derechos Humanos.

Fue militante del movimiento estudiantil durante la dictadura cívico-militar en Uruguay, integrando la Asociación Social y Cultural de Estudiantes de la Enseñanza Pública (ASCEEP)

Desde 1991 ejerce la docencia en Derechos Humanos en la Universidad de la República de Uruguay (UDELAR).

Militó en el Frente Amplio como independiente. Con el advenimiento del gobierno de Tabaré Vázquez en 2005 ocupó el cargo de Subsecretario (Viceministro) del Ministerio del Interior, cargo que ejerció hasta marzo de 2007. Después integró la Secretaría de Seguimiento de la Comisión para la Paz, a propuesta de Madres y Familiares de Detenidos Desaparecidos, hasta marzo de 2010. Fue integrante también de la Vertiente Artiguista hasta agosto de 2010.

Ha trabajado para la UNICEF, el PNUD, el Departamento de Asuntos Políticos de Naciones Unidas, la Comisión Interamericana de Derechos Humanos (CIDH), el BID, la OIM, la AECID, la ASDI, el IIDH y Save the Children.

En 2012 se desafilió del Frente Amplio para aceptar su postulación a integrar la Institución Nacional de Derechos Humanos y Defensoría del Pueblo (INDDHH), propuesto por la Red de Amigos de Luis «Perico» Pérez Aguirre. El 8 de mayo de 2012 fue votado por el Parlamento para integrar la INDDHH junto con Juan Raúl Ferreira Sienra, Ariela Peralta, Mariana González Guyer y Mirtha Guianze.

Referencias

Egresados de la Universidad de la República
Abogados de Uruguay
Políticos del Frente Amplio (Uruguay)
Nacidos en Montevideo

redpajama_set_name: RedPajamaWikipedia


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New Order + Pet Shop Boys kick off Unity Tour
New Order and Pet Shop Boys kicked off their North American tour last night.
The tour is a co-headline tour, so the bands will alternate as the opening and closing act each night and in addition to New Order and Pet Shop Boys, Paul Oakenfold warms up the crowds and plays between sets. The Unity Tour was originally planned for 2020, however the tour was postponed several times over the years due to the pandemic.
For last night's tour kickoff in Toronto New Order played the closing slot and ripped through a set filled with fan favorites from their classic era. Check out the setlist below.
The tour heads resumes in Boston tomorrow night.
New Order - 9/18/22
Your Silent Face
The Perfect Kiss
Be A Rebel
Bizarre Love Triangle