text:
\section{Our Approach}
\label{sec:approach}

We refer to a sequence of \emph{primitives} (low-level navigation and manipulation controllers)
as a \emph{program}.
To model the sequencing of primitives,
we first represent each object in the environment with a set of attributes
as described in Section~\ref{sec:attributes}.
In order to make programs generalizable, primitives should have the following
two properties.
First, each primitive should specialize in an atomic operation
such as moving close, pulling, grasping, and releasing.
Second, a primitive should not be specific to a single high-level task.
By limiting the role of each primitive and keeping it general,
many different manipulation tasks can be accomplished with the same small set of primitives,
and our approach becomes easily adaptable to different robots by providing implementation of
primitives on the new robot.

For illustration, we write a program for ``throw garbage away'' in Program \ref{alg:program_example}.
Most tasks could be written in such a format, where
there are many \texttt{if} statements inside the loop.
However, even for a simple ``throw garbage away''
task, the program is quite complex.
Writing down all the rules that can account for the many different scenarios that can arise in
a human environment would be quite challenging.

\vskip -.1in
\begin{algorithm}
	\caption{``throw garbage away.''}
	\label{alg:program_example}
\small
\begin{algorithmic}
   	\STATE {\bfseries Input:} environment $e$, trash $a_1$
   	\STATE $gc = find\_garbage\_can(e)$
   	\REPEAT
  	\IF{$a_1$ is in hand \& $gc$ is close}
	\STATE release($a_1$)
  	\ELSIF{$a_1$ is in hand \& far from $gc$}
	\STATE move\_close($gc$)
   	\ELSIF{$a_1$ is close \& $a_1$ not in hand \\
   		  \qquad \quad \& nothing on top of $a_1$}
   	\STATE grasp($a_1$) \\
	\quad \vdots

   	\ELSIF{$a_1$ is far}
	\STATE move\_close($a_1$)
	\ENDIF
   	\UNTIL{$a_1$ inside $gc$}
\end{algorithmic}
\end{algorithm}
\vskip -.1in

Program \ref{alg:program_example} is an example of what is commonly referred to as
reactive or dynamic
planning \cite{ai_book,koenig}. In traditional deliberative planning, a planning algorithm synthesizes a
sequence of steps that starts from the given state and reaches the given goal state.
Although current symbolic planners can find optimal plan sequences consisting of hundreds of steps,
such long sequences often break down because of unexpected events during the execution.
A dynamic plan provides a much more robust alternative.
At each step, the current state of the environment is considered
and the next appropriate action is selected by one of the conditional statements
in the main loop.
A well-constructed dynamic plan will identify the next step required to bring the robot closer
to the overall goal in any possible world state.
In complex domains, dynamic plans may become too complicated. However,
we are considering basic human activities, such as following a recipe,
where dynamic plans are generally quite compact and can effectively lead the robot to the
goal state. Moreover, as we will demonstrate, we can learn the dynamic plan
from observing a series of action sequences in related environments.

In order to make our approach more general, we introduce a feature based
representation for the conditions of \texttt{if} statements.
We can extract some features from both the environment and
the action that will be executed in the body of \texttt{if} statement.
With extracted features $\phi$ and some weight vector $\textbf{w}$ for each \texttt{if}
statement, the same conditional statements can be written as $\textbf{w}^T\phi$,
since the environment will always contain the rationale for executing certain primitive.
Such a feature-based approach allows us to re-write Program \ref{alg:program_example}
in the form of Program \ref{alg:program_example2}.

\vskip -.1in
\begin{algorithm}
	\caption{``throw garbage away.''}
	\label{alg:program_example2}
\small
\begin{algorithmic}
   	\STATE {\bfseries Input:} environment $e$, trash $a_1$
   	\STATE $gc = find\_garbage\_can(e)$
   	\REPEAT
	\STATE $e_t = $ current environment
  	\IF{$w_1^T \phi$($e_t$,release($a_1$)) $>$ 0}
	\STATE release($a_1$)
   	\ELSIF{$w_2^T \phi$($e_t$,move\_close($gc$)) $>$ 0}
	\STATE move\_close($gc$) \\
	\quad \vdots
   	\ELSIF{$w_n^T \phi$($e_t$,move\_close($a_1$)) $>$ 0}
	\STATE move\_close($a_1$)
	\ENDIF
   	\UNTIL{$a_1$ inside $gc$}
\end{algorithmic}
\end{algorithm}
\vskip -.1in

Now all the \texttt{if} statements have the same form, where the same primitive along with same arguments
are used in both the condition as well as the body of the \texttt{if} statement.
We can therefore reduce all \texttt{if} statements inside the loop
further down to a simple line which depends only on a single weight vector and
a single joint feature map, as shown in Program~\ref{alg:program_example3},
for finding the most suitable pair of primitive $\hat{p}_t$ and its arguments ($\hat{a}_{1,t}$, $\hat{a}_{2,t}$).
\vskip -.1in
\begin{algorithm}
	\caption{``throw garbage away.''}
	\label{alg:program_example3}
\small
\begin{algorithmic}
   	\STATE {\bfseries Input:} environment $e$, trash $g_{a1}$
    	\REPEAT
	\STATE $e_t = $ current environment
 	\STATE $(\hat{p}_t, \hat{a}_{1,t}, \hat{a}_{2,t}) :=
 		\displaystyle\argmax_{p_t  \in \mathcal{P}, a_{1,t}, a_{2,t} \in \mathcal{E}}
 		 {w^T\phi(e_t, p_t(a_{1,t}, a_{2,t}))}$
 	\STATE execute $\hat{p}_t( \hat{a}_{1,t}, \hat{a}_{2,t})$
	\UNTIL{$\hat{p}_t = done$}
\end{algorithmic}
\end{algorithm}
\vskip -.1in

The approach taken in Program \ref{alg:program_example3} also allowed removing
the function $find\_garbage\_can(e)$.
Both Program \ref{alg:program_example} and Program \ref{alg:program_example2}
require $find\_garbage\_can(e)$ which depends on
semantic labeling of each object in the environment.
The attributes of objects will allow the program
to infer which object is a garbage can without explicit encoding.

Program \ref{alg:program_example3} provides a generic representation of a dynamic plan.
We will now discuss an
approach to learning a set of weights.
To do so, we will employ a graph-like representation obtained by ``unrolling'' the
loop representing discrete time steps by different layers. We will obtain a representation
that is isomorphic to a Markov Random Field (MRF) and will use a maximum margin based
approach to training the weight vector.
Our MRF encodes the relations between the environment, primitive and its arguments.
Our empirical results show that such
a framework is effectively trainable with a relatively small set of example sequences. Our
feature-based dynamic plan formulation therefore offers an effective and general
representation to learn and generalize from action sequences, accomplishing high-level tasks
in a dynamic environment.

\section{Model Formulation}

We are given a set of possible primitives $\mathcal{P}$ (navigation and manipulation controllers)
to work with (see Section~\ref{sec:experiments})
and an environment $\mathcal{E}$ represented by a set of attributes.
Using these primitives, the robot has to accomplish a manipulation task $g \in \mathcal{T}$.
The manipulation task $g$ is followed by the arguments $g_{a1}, g_{a2} \in \mathcal{E}$
which give a specification of the task. For example, the program ``throw garbage away''
would have a single argument which would be the object id of the object that needs
to be thrown away.

At each time step $t$ (i.e., at each iteration of the loop in Program~\ref{alg:program_example3}),
our environment $e_t$ will dynamically change,
and its relations with the primitive is represented with a joint set of features.
These features include information about the physical and semantic properties of the
objects as well as information about their locations in the environment.

Now our goal is to predict the best primitive $p_t \in \mathcal{P}$ to execute
at each discrete time step, along with its arguments: $p_t(a_{1,t},a_{2,t})$.
We will do so by designing a score function $S(\cdot)$ that represents
the correctness of executing a primitive in the current environment for a task.
\begin{align*}
S(g(g_{a1}, g_{a2}),e_t, &p_t(a_{1,t},a_{2,t})) = \\
&w^T \phi(g(g_{a1}, g_{a2}),e_t, p_t(a_{1,t}, a_{2,t}))
\end{align*}

In order to have a parsimonious representation,
we decompose our score function using a model isomorphic
to a Markov Random Field (MRF), shown in Figure~\ref{fig:mrf}.
This allows us to
capture the dependency between primitives, their arguments, and environments
which are represented by set of attributes.
In the figure,
the top node represents the given task and its arguments ($g, g_{a1}, g_{a2}$).
The second layer from the top represents the sequence of primitives,
and the layer below represents the arguments associated with each primitive.
And, the bottom node represents the environment which is represented with set of attributes.
Note that we also take into account the previous two primitives in the past,
together with their arguments: $p_{t-1}(a_{1,t-1}, a_{2,t-1})$ and
$p_{t-2}(a_{1,t-2}, a_{2,t-2})$.

Now the decomposed score function is:
\begin{align*}
S =
\underbrace{S_{ae}}_{\mathclap{\text{args-env}}} +
\overbrace{S_{pt}}^{\mathclap{\text{prim-task}}} +
\underbrace{S_{aet}}_{\mathclap{\text{args-env-task}}} +
\overbrace{S_{pae}}^{\mathclap{\text{prim-args-env}\quad}} +
\underbrace{S_{ppt}}_{\mathclap{\quad\text{prim-prim(prev)-task}}} +
\overbrace{S_{paae}}^{\mathclap{\text{\quad prim-args-args(prev)-env}}}
\end{align*}
The terms associated with an edge in the graph are defined as
a linear function of its respective features $\phi$ and weights $w$:

\vskip -.2in
{\small
\begin{align*}
S_{ae} &= {w_{ae1}}^T \phi_{ae}(a_{1,t}, e_t) + {w_{ae2}}^T \phi_{ae}(a_{2,t}, e_t) \\
S_{pt} &= {w_{pt}}^T \phi_{pt}(p_{t}, g)
\end{align*}
}
Similarly, the terms associated with a clique in the graph are defined as
a linear function of respective features $\phi$ and weights $w$:

\vskip -.2in
{\small
\begin{align*}
S_{aet} &= {w_{aet1}}^T \phi_{aet}(a_{1,t}, e_{t}, g) + {w_{aet2}}^T \phi_{aet}(a_{2,t},e_{t},g) \\
S_{pae} &= {w_{pae1}}^T \phi_{pae}(p_{t}, a_{1,t}, e_{t}) + {w_{pae2}}^T \phi_{pae}(p_{t}, a_{2,t},e_{t}) \\
S_{ppt} &= {w_{ppt1}}^T \phi_{ppt}(p_{t-1}, p_{t}, g) + {w_{ppt2}}^T \phi_{ptt}(p_{t-2}, p_{t}, t) \\
S_{paae} &= \sum_{i,j\in (1,2), k \in (t-2, t-1)}{{w_{paae_{ijk}}}^T \phi_{paae}(p_{t}, a_{i,k}, a_{j,t}, e_t)}
\end{align*}
}
Using these edge and clique terms,
our score function $S$ can be simply written in the following form,
which we have seen in Program \ref{alg:program_example3} with an extra term $g$ for the task:
$S(g(g_{a1}, g_{a2}),e_t,p_t(a_{1,t},a_{2,t})) = w^T \phi(g(g_{a1}, g_{a2}),e_t, p_t(a_{1,t}, a_{2,t}))$.

\begin{figure}[tb!]
\centering
\includegraphics[width=0.37\textwidth]{mrf_factor.png}
\vspace*{\captionReduceTop}
\caption{
\textbf{Markov Random Field representation of our model} at discrete
time step $t$.
The top node represents the given task $g, g_{a1}, g_{a2}$.
The second layer from the top represents the sequence of primitives,
and the layer below represents the arguments associated with each primitive.
And, the bottom node represents the environment represented with set of attributes.
}
\vspace*{\captionReduceBot}
\label{fig:mrf}
\end{figure}

\input{features}

\begin{figure}[tb]

\centering
\includegraphics[width=0.48\columnwidth]{env2_enhanced.png}
\includegraphics[width=0.48\columnwidth]{env3_enhanced.png}
\vspace*{\captionReduceTop}
\caption{Figure showing two of our 13 environments in our evaluation dataset
		using 43 objects along with PR2 robot.}
\label{fig:env}
\vspace*{\captionReduceBot}
\end{figure}

\subsection{Learning}
We use a max-margin approach to train a single model for all tasks.
This maximum margin approach fits our formulation, since it assumes that
the discriminant function is a linear function of a weight vector $\textbf{w}$ and
a joint feature map $\phi(g(g_{a1}, g_{a2}),e_t, p_t(a_{1,t}, a_{2,t}))$, and it
has time complexity linear with the number of training examples when
solved using the cutting plane method \cite{joachims2009cutting}.
We formalize our problem as a ``1-slack'' structural SVM optimization problem:

{\small
\vskip -.2in
\begin{align*}
&\qquad \min_{\textbf{w}, \xi  \geq 0}{\frac{1}{2} \textbf{w}^T\textbf{w} + \frac{C}{l} \sum_{i=1}^{n}\sum_{t=1}^{l^i}\xi^i_t}\\
&s.t. \quad \text{for} \;\; 1\leq i \leq n, \text{for each time step} \; t: \;\;\;
\forall \hat{p} \in \mathcal{P},   \forall \hat{a}_1,\hat{a}_2 \in \mathcal{E}: \\
&\mathbi{w}^T {[\phi(g^i(g^i_{a1}, g^i_{a2}),e^i_t, p^i_t(a^i_{1,t}, a^i_{2,t}))\!-\!\phi(g^i(g^i_{a1}, g^i_{a2}),e^i_t, \hat{p} (\hat{a}_{1}, \hat{a}_{2}))]} \\
&\quad  \geq \Delta(\{ p^i_t, a^i_{1,t}, a^i_{2,t}\}, \{\hat{p}, \hat{a}_1, \hat{a}_2\}) - \xi^i_t
\end{align*}
\vskip -.1in
}

\noindent
where $n$ is the number of example sequences, $l^i$ is the length of the $i^{th}$ sequence,
and $l$ is the total length combining all sequences.
The loss function is defined as:

{\small
\vskip -.2in
\begin{align*}
\Delta(\{p, a_1, a_2\}, \{\hat{p}, \hat{a}_1, \hat{a}_2\})
& = \mathds{1}(p \neq \hat{p}) \!+\! \mathds{1}(a_1 \neq \hat{a}_1)
 \!+\! \mathds{1}(a_2 \neq \hat{a}_2)
\end{align*}
\vskip -.05in
}

With a learned $\textbf{w}$, we choose the next action in sequence
by selecting a pair of primitive and arguments that gives the largest discriminant value:
$$\argmax_{p_t  \in \mathcal{P}, a_{1,t}, a_{2,t} \in \mathcal{E}}
 		 {w^T \phi(g(g_{a1}, g_{a2}),e_t, p_t(a_{1,t}, a_{2,t}))}$$

\section{Experiments}
\label{sec:experiments}

\noindent
\textbf{Dataset.}
We considered seven primitives (low-level controllers):
\texttt{move\_close (A)}, \texttt{grasp (A)}, \texttt{release (A)},
\texttt{place\_above (A,B)}, \texttt{hold\_above (A,B)},
\texttt{follow\_traj\_circle (A)} and \texttt{follow\_traj\_pour (A,B)}.
Depending on the environment and the task, these primitives could
be instantiated with different arguments.  For example,
consider an environment that contains a bottle (obj04) containing liquid (obj16)
and an empty cup  (obj02) placed on top of the shelf, among other objects.
If, say from a recipe, our task is to pour the liquid, then our program
should figure out the correct sequence of primitives with correct arguments
(based on the objects' attributes, etc.):

{\scriptsize
\vskip -.2in
\begin{align*}
\{&\texttt{pour(obj16)};env2\} \rightarrow \\
\{&\texttt{move\_close(obj02); grasp(obj02); move\_close(obj04);}\\
&\texttt{place\_above(obj02,obj26); release(obj02); grasp(obj04);}\\
&\texttt{hold\_above(obj04,obj02); follow\_traj\_pour(obj04,obj02)}\}
\end{align*}
}
Note that the actual sequence does not directly interact with the liquid (obj16)---the only object specified by the task---but
rather with a container of liquid (obj04), an empty cup (obj02), and
a table (obj26), while none of these objects are specified in the task arguments.
As seen in this example, the input for our planning problem is  under-specified.

For evaluation, we prepared a dataset where the goal was to produce correct sequences
for the following tasks in different environments:
\begin{packed_item}

\item \texttt{stir(A)}:
Given a liquid A, the robot has to identify a stirrer of ideal size
(from several) and stir with it.
The liquid may be located on a tight shelf
where it would be dangerous to stir the liquid, and the robot should always stir it
on top of an open surface, like a table.
The robot should always
only interact with the container of the liquid, rather
than the liquid itself, whenever liquid needs to be carried or poured.
Our learning algorithm should learn such properties.

\item \texttt{pick\_and\_place(A,B)}:
The robot has to place A on top of B.
If A is under some other object C, the object C must first be moved
before interacting with object A.

\item \texttt{pour(A)}:
The robot has to identify a bowl-like object without object labels and pour liquid A into it.
Note again that liquid A cannot be directly interacted with,
and it should not be poured on top of a shelf.

\item \texttt{pour\_to(A,B)}:
The liquid A has to be poured into the container B. (A variant of the previous task
where the container B is specified but the model should be able to distinguish two different tasks.)

\item \texttt{throw\_away(A)}:
The robot has to locate a garbage can in the environment and throw out object A.
\end{packed_item}

In order to learn these programs,
we collected 127 sequences for 113 unique scenarios by presenting participants the environment in simulation
and the task to be done.
We considered a single-armed
mobile manipulator robot for these  tasks.
In order to extract information about the environment at each time frame of every sequence,
we implemented each primitive using OpenRAVE simulator \cite{diankov_thesis}.
Though most of the scenarios
had a single optimal sequence, multiple sequences were introduced when there
were other acceptable variations. The length of each sequence varies from 4 steps to 10 steps,
providing a total of 736 instances of primitives.
To ensure variety in sequences, sequences were generated based on the 13 different environments
shown in Figure~\ref{fig:env}, using 43 objects each with unique attributes.

\begin{figure*}[t]
\subfigure[\textbf{Confusion matrix} for the seven primitives in our dataset.
        Our dataset consist of 736 instances of seven primitives in 127 sequences
        on five manipulation tasks.]{
\includegraphics[width=0.29\textwidth, height=1.45in]{confmat.png}
\label{fig:confmat}
}
\;
\subfigure[\textbf{Percentage of programs correct.} Without any feedback in completely
autonomous mode, the accuracy is 69.7\%.  With feedback (number of
feedbacks on x-axis), the performance increases.  This is on full
127 sequence dataset.]{
\includegraphics[width=0.30\textwidth, height=1.45in]{feedback1.png}
\label{fig:feedback1}
}
\;
\subfigure[\textbf{Percentage of programs correct for 12 high-level tasks} such as making sweet tea.
In completely
autonomous mode, the accuracy is 75\%.  With feedback (number of
feedbacks on x-axis), the performance increases.]
{\includegraphics[width=0.30\textwidth, height=1.45in]{feedback2.png}
\label{fig:feedback2}
}
\vspace*{\captionReduceTop}
\caption{\textbf{Results with cross-validation.}
(a) On predicting the correct primitive individually.
(b) On predicting programs, with and without user intervention.
(c) On performing different tasks with the predicted sequences.
}
\vspace*{\captionReduceBot}
\label{fig:graphs}
\end{figure*}

\noindent
\textbf{Baseline Algorithms.}
We compared our model against following baseline algorithms:
\begin{packed_item}
\item \textit{chance}:
At each time step, a primitive and its arguments are selected at random.

\item \textit{multiclass}:
A multiclass SVM \cite{joachims2009cutting} was trained to predict primitives
without arguments, since the set of possible arguments changes depending on the environment.

\item \textit{symbolic-plan-svm}:
A PDDL-based symbolic planner \cite{yang2007learning, zhuo2010learning}
requires a domain and a problem definition.
Each scenario was translated to symbolic entities and relations. However,
the pre-conditions and effects of each action in domain definition were hand-coded,
and each object was labeled with attributes using predicates.
Unlike our model that works on an under-specified problem,
each symbolic planning problem requires an explicit goal state.
In order to define these goal states, we have trained
ranking SVMs \cite{joachims2006training} in order to
detect a `stirrer', an `object to pour into' and a `garbage can'
for \texttt{stir}, \texttt{pour}, and \texttt{throw\_away}, respectively.
Each symbolic planning instance was then solved by reducing to a satisfiability problem
\cite{kautz1992planning, rintanen2012planning}.

\item \textit{symbolic-plan-manual}:
Based on the same method as \textit{symbolic-plan-svm}, instead of training ranking SVMs,
we provided ground-truth goal states.
Even after providing lots of hand-coded rules, it is still missing some rules
due to the difficulty of representation using PDDL \cite{yang2007learning, zhuo2010learning},
These missing rules include the fact that liquid needs to be handled
through its container and
that objects should not be manipulated on top of the shelf.

\end{packed_item}

\noindent
\textbf{Evaluation and Results.}
We evaluated our algorithm through \emph{6-fold cross-validation}, computing accuracies
over primitives, over primitives with arguments, and over the full sequences.
Figure \ref{fig:confmat} shows the confusion matrix for prediction
of our seven primitives. We see that our model is quite robust for most primitives.

With our dataset, our model was able to correctly predict pairs of primitives and arguments
90.0\% of the time and full sequences 69.7\% of the time (Table \ref{tab:args}).
Considering only the primitives without arguments,
it was able to predict primitive 96.7\% of the time and full sequence 91.6\% of the time.
The last column of Table \ref{tab:args} shows the performance with respect to whether the complete
sequence was correct or not.
For example, for ``pouring'', our model has learned not only to bring a cup over to
the table, but also to pick out the cup when there are multiple other objects like a pot, a bowl, or a can
that may have similar properties.

\textbf{How do baselines perform for our under-specified planning problem?}
The results of various baseline algorithms are shown in Table \ref{tab:args}.
If the primitive and arguments pairs are predicted at random,
none of the sequences would be correct because of the large search space of arguments.
\textit{Multiclass} predicted well for some of the primitives but
suffered greatly on primitives like
\texttt{place\_above}, \texttt{hold\_above} and \texttt{follow\_traj\_pour},
which drastically impacts constructing overall sequences, even with correct arguments
selected.

The symbolic planner based approaches, \textit{symbolic-plan-svm} and \textit{symbolic-plan-manual},
suffered greatly from under-specified nature of the problem.
The planners predicted correctly 49.6\% and 54.9\% of the times, respectively,
compared to our model's performance of 69.7\%.
Even though both planners made use of heavily hand-coded domain definitions of the problem,
due to the nature of the language used by symbolic planners, rules such as that liquid should not be
handled on top of shelves were not able to be encoded.
Even if the language were capable of encoding these rules, it would require a human expert
in planning language to carefully encode every single rule the expert can come up with.

Also, by varying the set of features,
it is evident that without very robust primitive-level accuracies,
the models are unable to construct a single correct sequence.

\textbf{How important is attribute representation of objects?}
For 113 unique scenarios in our dataset,
we have randomly flipped binary attributes and observed the effects of detection errors
on correctness for the full sequence (Figure~\ref{fig:artnoise}).
When there is no error in detecting attributes, our model performs at 69.7\%.
With 10\% detection error, it performs at 55.8\%,
and with 40\% detection errors, it performs at 38.1\%.
Since the attribute detection is more reliable than the object detection
\cite{ferrarilearning, farhadi2009describing, lampert2009learning},
our model will perform better than planners based on explicit object labels.

\begin{figure*}[tbh!]
\vskip -.05in
\centering
\includegraphics[width=0.95\textwidth,height=34mm]{image_tea_pr2.png}
\vspace*{\captionReduceTop}
\caption{\textbf{Few snapshots of learned sequences} forming the higher level
task of serving sweet tea, which takes the sequence of pouring tea into a cup,
pouring sugar into a cup, and then stirring it.}
\label{fig:exampletask}
\vspace*{\captionReduceBot}
\end{figure*}

\textbf{How can the robot utilize learned programs?}
These learned programs can form higher level tasks such as making
a recipe found online. For example, serving sweet tea would require the
following steps: pouring tea into a cup, pouring sugar into a cup, and stirring it
(Figure~\ref{fig:exampletask}).
We have tested each of the four tasks,
\emph{serve-sweet-tea}, \emph{serve-coffee-with-milk},
\emph{empty-container-and-throw-away}, and \emph{serve-and-store},
in three environments.
Each of the four tasks can be sequenced in following manner by programs respectively:
\texttt{pour} $\rightarrow$ \texttt{pour\_to} $\rightarrow$ \texttt{stir},
\texttt{pour\_to} $\rightarrow$ \texttt{pour\_to},
\texttt{pour} $\rightarrow$ \texttt{throw\_away},
and \texttt{pour} $\rightarrow$ \texttt{pick\_and\_place}.
Out of total 12 scenarios, our model was able to
successfully complete the task for 9 scenarios.

\begin{figure}[tb!]
\vskip -3mm
 \centering
\includegraphics[width=0.9\columnwidth]{artificial_noise.png}
\vspace*{\captionReduceTop}
\caption{\textbf{Effect of attribute perception error.}
Figure showing percentage of programs correct with attribute labeling errors
for binary attributes.
For 113 unique scenarios, binary attributes were randomly flipped.}
\vspace*{\captionReduceBot}
\vspace*{\captionReduceBot}
\label{fig:artnoise}
\end{figure}

\textbf{Does the robot need a human observer?}
In an assistive robotics setting, a robot will be accompanied by a human
observer. With help from the human, performance can be greatly improved.
Instead of choosing a primitive and argument pair
that maximizes the discriminant function, the robot can present the top 2 or 3
primitive and argument pairs to the observer, who can simply give feedback on the
best option among those choices.
At the initial time step of the sequence,
with only a single piece of feedback, given 2 or 3 choices,
performance improves to 74.1\% and 75.6\% respectively from 69.7\%
(Figure \ref{fig:feedback1}).
If feedback was provided through whole sequence with the top 2 or 3 choices,
it further improves to 76.7\% and 81.4\%.
Furthermore, the four higher level tasks (recipes) considered earlier
also shows that with a single feedback at the initial time step of each program,
the results improve from 75\% to 100\% (Figure \ref{fig:feedback2}).

\smallskip
\noindent
\textbf{Robotic Experiments.}
Finally, we demonstrate that our inferred programs
can be successfully executed on our Kodiak PR2 robot
for a given task in an environment.
Using our implementation of the primitives discussed in Section~\ref{sec:experiments},
we show our robot performing the task of ``serving sweet tea." It comprises
executing three programs in series -- \texttt{pour}, \texttt{pour\_to} and \texttt{stir} --
which in total required sequence of 20 primitives with correct arguments.
Each of these programs (i.e., the sequence of primitives and arguments) is inferred
for this environment. Figure~\ref{fig:exampletask} shows a few snapshots and the full video is
available at:\\
\texttt{\small http://pr.cs.cornell.edu/learningtasksequences}

\subsection{Features}
\vspace*{\subsectionReduceBot}

In this section, we describe our features $\phi(\cdot)$ for the different terms in the previous section.

\emph{Arguments-environment ($\phi_{ae}$)}:
The robot should be aware of its location and the current level of its interaction
with objects (e.g., grasped), which are given as possible primitive arguments $a_{1,t}, a_{2,t}$.
Therefore, we add two binary features which
indicate whether each primitive argument is already grasped
and two features for the centroid distance from the robot to each primitive arguments.

For capturing spatial relation between
two objects $a_{1,t}$ and $a_{2,t}$,
we add one binary feature indicating
whether primitive arguments $a_{1,t}, a_{2,t}$ are currently in collision with each other.

\emph{Arguments-environment-task ($\phi_{aet}$)}:
To capture relations between the objects of interest (task arguments) and
objects of possible interest (primitive arguments),
we build a binary vector of length 8.
First four represents the indicator values of whether the objects of interest
are identical as the objects of possible interest, and the last four
represents spatial relation of whether they overlap from top view.

It is important to realize the type of object that is below the objects of interests,
and the desired property (e.g., bowl-like object or table-like object)
may differ depending on the situation.
We create two feature vectors, each of length $l$.
If the robot is holding the object, we store its extracted attributes in the first vector.
Otherwise, we store them in the second vector.
If the primitive has two arguments, we use the first primitive argument
since it often has higher level of interaction with the robot
compared to the second argument.

Finally, to capture correlation between the high-level task and
the types of object in primitive argument,
we take a tensor product of two vectors:
an attribute vector of length $2l$ for two objects and
a binary occurrence vector of length $|\mathcal{T}|$.
The matrix of size $2l \times |\mathcal{T}|$ is flattened to a vector.

\emph{Primitive-task ($\phi_{pt}$)}:
The set of primitives that are useful may differ depending on the type of the task.
We create a $|\mathcal{T}|\times|\mathcal{P}|$ binary co-occurrence matrix
between the task $g$ and the primitive $p_t$
that has a single non-zero entry in the
current task's ($g^{th}$) row and current primitive's (${p_t}^{th}$) column.

\emph{Primitive-arguments-environment ($\phi_{pae}$)}:
Some primitives such as \texttt{hold\_above} require one of the objects in arguments
to be grasped or not to be grasped
to execute correctly.
We create a $|\mathcal{P}|\times 2$ matrix where the row for
the current primitive (${p_t}^{th}$ row)
contains two binary values indicating
whether each primitive argument is in the manipulator.

\emph{Primitive-primitive(previous)-task ($\phi_{ppt})$}:
The robot makes different transitions between primitives for different tasks.
Thus, a binary co-occurrence matrix of size $|\mathcal{T}|\times|\mathcal{P}|^2$
represents transition occurrence between the primitives for each task.
In this matrix, we encode two transitions for the current task $g$,
from $t-2$ to $t$ and from $t-1$ to $t$.

\emph{Primitive-arguments-arguments(previous)-environment ($\phi_{paae}$)}:
For a certain primitive in certain situations, the arguments
may not change between time steps.
For example, \texttt{pour(A,B)} would often be preceded by \texttt{hold\_above (A,B)}.
Thus, the matrix of size $|\mathcal{P}|\times 8$ is created,
with the ${p_t}^{th}$ row containing 8 binary values representing
whether the two primitive arguments at time $t$ are the same as
the two arguments at $t-1$ or the two arguments at $t-2$.

\subsection{Attributes.}
\label{sec:attributes}

Every object in the environment including tables and the floor
is represented using the following set of attributes:
height $h$, max(width($w$),length($l$)), min($w, l$), volume($w*l*h$),
min($w,l,h$)-over-max($w,l,h$), median($w,l,h$)-over-max($w,l,h$),
cylinder-shape, box-shape, liquid, container, handle, movable,
large-horizontal-surface, and multiple-large-horizontal-surface.
Attributes such as cylinder-shape, box-shape, container, handle, and
large-horizontal-surface can be reliably extracted from RGB or RGBD images, and were shown
to be useful in several different applications
\cite{ferrarilearning, farhadi2009describing, lampert2009learning, koppula2011semantic}.
We study the effects of attribute detection errors on our model in Section~\ref{sec:experiments}.

\section{Introduction}

When interacting with a robot, users often under-specify the tasks to be performed.
For example in Figure~\ref{fig:exampletask}, when asked to \texttt{pour} something,
the robot has to infer
which cup to pour into and
a complete sequence of the navigation and manipulation
steps---moving close, grasping, placing, and so on.

This sequence not only changes with the task, but also with the perceived state
of the environment.
As an example, consider the task of a robot fetching a magazine from a desk.
The method to perform this task varies
depending on several properties of the environment: for example,
the robot's relative distance from the magazine, the robot's relative
orientation, the thickness of the magazine, and the presence or the absence of
other items on top of the magazine. If the magazine is very thin, the
robot may have to slide the magazine to the side of the table to pick
it up. If there is a mug sitting on top of the magazine, it would have
to be moved prior to the magazine being picked up.
Thus, especially when the details of the manipulation task are under-specified,
the success of executing the task
depends on the ability to detect the object and
on the ability to sequence the set of \emph{primitives} (navigation and manipulation controllers)
in various ways in response to the environment.

In recent years, there have been significant developments
in building low-level controllers for robots
\cite{thrun2005probabilistic} as well as in perceptual tasks such as
object detection from sensor data \cite{koppula2011semantic,jiang-hallucinatinghumans-labeling3dscenes-cvpr2013,wulenzsaxena2014_hierarchicalrgbdlabeling}.
In this work, our goal is to, given the environment and the task,
enable robots to sequence the navigation and manipulation primitives.
Manually sequencing instructions
is not scalable because of the large
variety of tasks and situations that can arise in unstructured environments.

In this work, we take an attribute-based representation of the environment,
where each object is represented with a set of
attributes, such as their size, shape-related information, presence of
handles, and so forth.
For a given task, there are often multiple objects with similar
functions that can be used to accomplish the task,
and humans can naturally reason and choose the most
suitable object for the given task \cite{kemp2010learning}.
Our model, based on attribute representation of objects,
is similarly capable of choosing the most
suitable object for the given task among many objects in the environment.

\begin{figure}[tb!]
\centering
\includegraphics[width=0.98\columnwidth]{pr2_scene.png}
\vspace*{\captionReduceTop}
\caption{Figure showing our Kodiak PR2 in a kitchen with different objects labeled
with attributes. To accomplish the under-defined task of \texttt{pour(obj17)},
it has to first find the mug (obj13) and carry it to the table (obj05)
since it is dangerous to pour liquid in a tight shelf. Once the mug is
on the table, it has to bring the liquid by the container (obj19) and then
finally pour it into the mug.}
\label{fig:exampletask}
\end{figure}

We take a dynamic planning approach to the problem of synthesizing, in
the right order, the suitable primitive controllers.
The best primitive
to execute at each discrete time step is based on a score function
that represents the appropriateness of a particular primitive for the
current state of the environment.  Conceptually, a dynamic plan
consists of a loop containing a sequence of conditional statements
each with an associated primitive controller or action. If the current
environment matches the conditions of one of the conditional
statements, the corresponding primitive controller is executed,
bringing the robot one step closer to completing the overall task
(example in Section~\ref{sec:approach}).
We will show how to generalize sequencing of primitives to make
them more flexible and robust, by switching to an attribute-based
representation.
We then show how to unroll the loop into a graph-based
representation, isomorphic to a Markov Random Field.
We then train the parameters of the model by maximum margin learning
method using a dataset comprising many examples of sequences.

We evaluated our model on 127 controller sequences for
five under-specified manipulation tasks generated from 13 environments using 7
primitives.  We show that our model can predict suitable primitives to
be executed with the correct arguments in most settings. Furthermore,
we show that, for five high-level tasks,
our algorithm was able to correctly sequence 70\% of the sequences in different environments.

The main contributions of this paper are:
\begin{packed_item}
\item using an attribute-based representation of the environment for task planning,
\item inferring the sequence of steps where the goals are under-specified and have to be inferred from the context,
\item a graph-based representation of a dynamic plan by unrolling the loop into a Markov Random Field.
\end{packed_item}

\section{Related Work}

There is a large body of work in task planning across various communities. We describe
some of them in the following categories.

\noindent
\textbf{Manual Controller Sequencing.}
Many works manually sequence different types of controllers
to accomplish specific types of tasks.
\citet{bollini2011bakebot} develop an end-to-end system which can find ingredients
on a tabletop and mix them uniformly to bake cookies.
Others used pre-programmed sequences for tea serving and carrying humans
in healthcare robotics \cite{nakai2006development,mukai2010development}.
These approaches however cannot scale to large number of tasks when each task
requires its own complicated rules for sequencing controllers
and assumes a controlled environment,
which is very different from actual human households, where objects of interest
can appear anywhere in the environment
with a variety of similar objects.

\citet{beetz2011robotic} retrieve a sequence for ``making a pancake'' from online websites
but assumes an environment with correct labels and a single choice of object for the task.
Human experts can generate finite state machines for robots but
this again requires explicit labels (e.g. AR tags) \cite{nguyen2013ros}.
Our work addresses these problems by representing each object in the environment as a set
of attributes which is more robust than labeling the individual object
\cite{ferrarilearning, farhadi2009describing, lampert2009learning}.
In our recent work \cite{misra_tellmedave_2014},
we learn a sequence given a natural language instruction and object labels,
where the focus is to learn the grounding of
the natural language into the environment.

\noindent
\textbf{Learning Activities from Videos.}
In the area of computer vision,
several works \cite{yang2010recognizing,yao2010modeling,sung_rgbdactivity_2012,koppula2013_anticipatingactivities}
consider modeling the sequence of activities
that humans perform.  These works are complementary to ours because our problem is to infer
the sequence of controllers and not to label the videos.

\noindent
\textbf{Symbolic Planning.}
Planning problems often rely on symbolic representation of entities as well as their relations.
This has often been formalized as a deduction \cite{green1969application}
or satisfiability problem \cite{kautz1992planning}.
A plan can also be generated hierarchically by first planning
abstractly, and then generating a detailed plan recursively \cite{kaelbling2011hierarchical}.
Such approaches can generate a sequence of controllers that can be proven
to be correct \cite{johnson2011probabilistic,belta2007symbolic}.
Symbolic planners however require encoding every precondition and effect of each operation,
which will not scale in human environments where there are large variations.
Such planners also require domain description for each planning domain
including the types of each object (e.g., pallet crate - surface, hoist surface - locatable)
as well as any relations
(e.g., on x:crate y:surface, available x:hoist).
The preconditions and effects
can be learned directly from examples of recorded plans \cite{yang2007learning, zhuo2010learning}
but this method suffers when there is noise in the
data \cite{zhuo2010learning}, and also suffers from the difficulty of
modeling real world situations with the PDDL representation \cite{yang2007learning}.

Such STRIPS-style representation also restricts the environment to be represented
with explicit labels.
Though there is a substantial body of work on labeling human environments
\cite{koppula2011semantic, lai2012detection}, it still remains a challenging task.
A more reliable way of representing an environment is representing
through attributes \cite{ferrarilearning, farhadi2009describing}.
An attribute-based representation
even allows classification of object classes that are not present in the training data \cite{lampert2009learning}.
Similarly, in our work, we represent the environment as a set of attributes,
allowing the robot to search for objects with the most suitable attributes
rather than looking for a specific object label.

\noindent
\textbf{Predicting Sequences.}
Predicting sequences has mostly been studied in a Markov Decision Process framework,
which finds an optimal policy given the reward for each state.
Because the reward function cannot be easily specified in many applications,
inverse reinforcement learning (IRL)
learns the reward function from an expert's policy \cite{ng2000algorithms}.
IRL is extended to Apprenticeship Learning
based on the assumption that the expert tries to optimize an unknown reward function \cite{abbeel2004apprenticeship}.
Most similar to our work, the Max-Margin Planning
frames imitation learning as a structured max-margin learning problem \cite{ratliff2006maximum}.
However, this has only been applied to problems such as 2D path planning,
grasp prediction and footstep prediction \cite{ratliff2009learning}, which
have much smaller and clearer sets of states and actions compared to
our problem of sequencing different controllers.
Co-Active Learning for manipulation path planning \cite{jainsaxena2013_trajectorypreferences},
where user preferences are learned from weak incremental feedback,
does not directly apply to sequencing different controllers.

Both the model-based and model-free methods evaluate state-action pairs.
When it is not possible to have knowledge about all possible or subsequent states (\emph{full backup}),
they can rely on \emph{sample backup}
which still requires sufficient sample to be drawn from the state space \cite{qualitative_rl_book}.
However, when lots of robot-object interactions are involved,
highly accurate and reliable physics-based robotic simulation is required along
with reliable implementation of each manipulation controllers. Note that each of
the manipulation primitives such as grasping are still not fully solved problems.
For example, consider the scenario where the robot is grasping the edge of the table and
was given the instruction of \texttt{follow\_traj\_pour(table,shelf)}.
It is unclear what should occur in the environment and becomes challenging to have reliable simulation
of actions.
Thus, in the context of reinforcement learning, we take a maximum margin
based approach to learning the weight for $\textbf{w}^T \phi(s,a)$ such that it maximizes the number of states
where the expert outperforms other policies,
and chooses the action that maximizes $\textbf{w}^T \phi(s,a)$ at each time step.
The key in our work is representing task planning as a graph-based model and designing
a score function that uses attribute-based representation of environment for under-specified tasks.

\section{Conclusion}

In this paper, we considered the problem of learning sequences of
controllers for robots in unstructured human environments.
In an unstructured environment, even a simple task such as pouring
can take variety of different sequences of controllers
depending on the configuration of the environment.
We took a
dynamic planning approach, where we represent the current state of the
environment using a set of attributes.
To ensure that our dynamic plans are as general and flexible as possible,
we designed a score function that captures relations between task,
environment, primitives, and their arguments,
and we trained a set of parameters weighting the various attributes from example sequences.
By unrolling the program, we can obtain a Markov Random
Field style representation, and use a maximum margin learning
strategy. We demonstrated on a series of example sequences that our
approach can effectively learn dynamic plans for various complex
high-level tasks.

\section*{Acknowledgements}
This work was supported in
part by ONR Grant N00014-14-1-0156, and Microsoft Faculty
Fellowship and NSF Career award to Saxena.

{
\small
\bibliographystyle{abbrvnat}

redpajama_set_name: RedPajamaArXiv


text:
if [[ $TERM == "xterm-termite" ]]; then
  eval $(dircolors $XDG_CONFIG_HOME/termite/dircolors)
fi

redpajama_set_name: RedPajamaGithub


Week 6 of build season is just wrapping up as we head into the next week towards STOP build day on February 20th. Although our build season has been busy and full of learning opportunities, we have had some monumental changes for the team.
Mechanical was able to complete two robots and have learned important skills and necessities moving forward as a team.
Revolutionary development took place in programming and is currently being implemented. Many of the sensors established this year will assist us in our autonomous and teleop code.
Scouting is testing a newly implemented system on tablets that has been proven to be reliable and a potential solution for the challenges many teams face scouting.
Business and safety have also created some fantastic themed promotional materials to go along with this year's game, PowerUp and giving our team an arcade-like appearance.


Listen and download Mounam Pesiyathey Super... ringtone for your mobile phone. This ringtone was uploaded by manoj to Tamil ringtones.
This Tamil ringtone Mounam Pesiyathey Super... was uploaded and shared by on of our user so that you can enjoy and download it.


It's one of the biggest times of the year for the volunteering sector. For 7 days the media gives volunteering the attention it deserves and urges everyone to get out and give back.
But what does National Volunteer Week mean for nonprofits?
Many organizations take the opportunity to organize extra-special volunteering days to attract new community members to the cause, focusing on large groups and high-profile activities that will ensure media pick-up. But National Volunteer Week isn't just about parties and one-off volunteer days (although these are great!) No, the real meat of this annual event is in the relationships.
Your relationships with your volunteers are some of the most important that your organization has. National Volunteer Week is an opportunity to take some time to reflect on these relationships, to appreciate them, and to strengthen them.
Plan a volunteer appreciation event. Here's an article with some good tips.
Take advantage of the big buzz about volunteering and create some new volunteer opportunities for new supporters. You can post them on VolunteerMatch using keyword "National Volunteer Week," and they'll be streamed on our special seasons of service page.
Just as your volunteers strive to do their best for you, show your respect for them by learning all the skills you need to run a great volunteer program. There are now 17 free webinars available in the VolunteerMatch Learning Center.
Update your listings on VolunteerMatch so your dedicated volunteers can find you more easily – after all, relationships are a two-way street.
Share the stories of your volunteers, both new and old, and what they've done for your organization.
As Carey Fritz pointed out in her recent article about volunteer management, many organizations don't recognize the key place a volunteer program has in the fulfillment of their mission. It's easy to get bogged down in dollars and cents, especially in rough economic times. Take the opportunity of National Volunteer Week to get everyone at your organization focused on what matters: your relationships with your volunteers.
As members of the VolunteerMatch community, you're already very familiar with this universal truth – that volunteers are at least as valuable as both silver and gold, if not more so.


Give for the Goal thanks USA Junior Soccer / LA Galaxy OC for generously donating toward Project Nigeria 2017.
Mageeco Football Academy serves the greater community in Lagos, Nigeria. The players ages span from as young as four years old up to the age of twenty-one. The club currently serves a larger recreational soccer community as well as elite teams in the older age groups.


The above information about Israel Aliyah Center, located at the address 126 High St, Boston, MA, is accurate and has been gathered from open sources. To contact the company via phone and get all the necessary information, call (617) 457-8750. You may visit the website http://www.jewishagency.org/ to obtain additional information about the company.
If you wish to personally visit the office, enter the following GPS-coordinates into your car navigation system: 42.35544500, -71.05400800.
Information about the company is also available by visiting on the following categories on this website: Orthodox Churches, Pentecostal Churches, Reformed Churches, Buddhist Temples, Hindu Temples, Messianic Synagogues, Orthodox Synagogues, Synagogues.
Near this company, there are 5 similar companies that may be of interest to you. The closest company is called American Jewish Committee and is located at a distance of 0.0346 miles; 0.0556 kilometers. The company farthest away is called Citylife Presbyterian Church and is located at a distance of 0.2700 miles; 0.4345 kilometers away.


text:
Understanding Anger
Date Friday - July 24 2009
GuestsDr. Les Carter
Filling in for George, Ian Punnett welcomed Dr. Les Carter, during the first two and a half hours of the program, for a discussion on the nature of anger, how to use it constructively and avoid being consumed by it. Despite the negative connotations surrounding the emotion, Carter noted that "anger prompts us to get tapped into our sense of self-preservation." If channeled in a positive way, he suggested that one can have "anger working on their behalf" and, thus, diffuse the situation which originally drew their ire.
However, he said, it is when one does not know how to constructively use that anger that things can go awry. He lamented that "the mismanaged anger just perpetuates and creates more and more reasons for the emotion to be there." Compounding the issue is that, when a person gets angry, certain physiological effects occur such as a rush of adrenaline and changes in their respiratory or digestive systems. People who are "rageaholics" can suffer from physical ailments related to repeated bouts of anger and addiction to the "rush" of being angry.
Carter stressed the importance of parents teaching young people how to both recognize and cope with anger. He advised that adults respond to an angry child by saying "let's discuss the options you have." They should then weigh the outcomes of each potential reaction and help steer them towards a positive solution. While one such instance may not change their perspective on anger, he emphasized that there are "hundreds and hundreds of opportunities" throughout a child's life to instill this pragmatic mindset. Sadly, he speculated that only 15 to 20% of children get this kind of guidance and even that figure is "probably being generous."
The final 90 minutes of the program featured Open Lines.
drlescarter.com
The Anger Trap
Enough About You, Let's Talk About Me
Venomous spit and an electrical discharge are just two of the fearsome tactics attributed to the legendary cryptobeast known as the Mongolian Death Worm. While not as famous as its larger siblings like Bigfoot or Nessie, the MDW has been gaining in popularity in the cryptozoology world as more witnesses' stories emerge and investigators journey to Asia to hunt the infamous invertebrate. You can find out about one group's quest to track down the elusive creature here.
The Grave And The Constant
Sexopolis
Jean-Pierre Mirouze
David Holmes
Come On Get Happy
Partridge Family
Praise You
Fat Boy Slim
Bipedal Canine Creatures
Madame Blavatsky & The Secret Doctrine

redpajama_set_name: RedPajamaCommonCrawl


text:
Dalmau González Albiol, de vegades amb el nom artístic de Dalmacio González o Gonzales (Olot, Garrotxa, 1940), és un tenor català que ha tingut una destacada carrera de tenor líric lleuger i reconegut com un dels tenors més importants del repertori rossinià.

Va començar els estudis musicals i de cant a la Schola Cantorum del seu col·legi de Girona. Posteriorment va completar la seva formació musical a Barcelona al Conservatori del Liceu, al Conservatori Municipal i al Mozarteum de Salzburg, fent estudis de cant amb el gran mestre Jaume Francisco Puig.

El 1979 va debutar al Metropolitan de Nova York a Don Pasquale i en posteriors temporades Il barbiere di Siviglia, Falstaff, L'elisir d'amore, i Rigoletto (les dues darreres en substitució de Luciano Pavarotti). També ha cantat a La Scala, Torí, Roma, Verona, Parma, Nàpols, Trieste, Bolonya, Florència, RAI, Catània, Modena, Pesaro, Viena, Graz, Hatberg, Hamburg, Munic, Bonn, Hannover, Stuttgart, Düsseldorf, Berlín, Madrid, Bilbao, Oviedo,Carnegie Hall, San Francisco, Los Angeles, Buenos Aires, Xile, Caracas, Praga, Montevideo, París, Aix-en-Provence, etc. L'han dirigit mestres i directors com ara Claudio Abbado, James Levine, Carlo Maria Giulini, Nevil Marriner, Maurizio Pollini, Ralf Weikert, Jesús López Cobos, Antoni Ros Marbà, etc.

Referències

Cantants de les comarques gironines contemporanis
Tenors catalans
Alumnes del Conservatori del Liceu
Tenors que han actuat al Liceu
Músics olotins
Naixements del 1940

redpajama_set_name: RedPajamaWikipedia


As Azusa Pacific University works to meet federal and state authorization requirements in states outside California, APU's home state, we want to bring to your attention that APU's online programs are not available in all states. Through the information listed below, we hope to assist you in better meeting your academic goals.
State authorization of distance education is a complex and dynamic environment. Prospective students should check back for updates.
If APU cannot currently provide distance education in your state, please check back soon, as the information here changes often.
Specific questions about APU's State Authorization of Distance Education can be directed to gpadmissions@apu.edu.


text:
Jonathan begins assessment of flood ravaged areas, urge victims not to commit suicide
President Goodluck Jonathan has urged victims of the flood disaster in Kogi state not worry or commit suicide over the loss of property, as they … Continue reading Jonathan begins assessment of flood ravaged areas, urge victims not to commit suicide
By Channels Television
President Goodluck Jonathan has urged victims of the flood disaster in Kogi state not worry or commit suicide over the loss of property, as they have a responsible government that cares for the people.
Aerial view of flooded area in Kogi state
Jonathan who made this call while speaking at St Luke Primary Schools, one of the several relief camps in the State, appealed to owners of property destroyed by the flood not to bother themselves as the government will cushion the effects of the flood in all the affected states.
The president, who was earlier on spot assessment of Ibaji and Idah Local government areas which were completely submerged by water, stated that federal government is planning to build more dams as measure to control future occurrence.
He described the ravaging flood as a natural disaster, stressing that the magnitude this year's flood was last witnessed in 1969.
The president also explained that part of Federal Government efforts to cushion the devastating effects in all the affected states of the Federation, is the inauguration of a high power committee headed by the President of Dangote groups, Alhaji Aliko, to raise funds which will mitigate against the effects of the of the flood.
President Jonathan landed at the Maigumeri Barracks in an Hellicopter at about 11AM, accompanied by the Minister of Environment and the Director-General of the National Emergency Agency (NEMA).
Arriving Lokoja, the state capital, the president and the state governor, Captain Idris Wada in his address, told the president that he has approached the Ministers of Works, Mines and Power to release some of the housing estate that belong to the federal government and the abandoned houses in Ajaokuta for the relocation of the affected people.
President Jonathan with a day-old baby born in the Kogi flood relief camp
President Goodluck Jonathan before giving his speech, demanded to see some of the 23 babies born that were delivered in the relief camp. He also gave an opportunity to the leader of the relief camp to address him.
While thanking the victims for their patience with the government, he pointed out that he was in the state to commiserate with them and the state government over the devastating effects of the flood in the nine affected local governments areas.
According to the President, the flood was linked up to global warming.
He commended the state governor for his efforts to provide the alternative relief camps and materials for the affected.
The president also lauded all other emergency agencies such as NEMA, SEMA, Red Cross, NGOs and all the development partners who have assisted in managing the disaster.
Africa Ajaokuta Alhaji Aliko Dangote channels television Channels TV Dangote Groups Goodluck Jonathan Ibaji Idah Idris Wada Kogi State Lokoja NEMA NGOs Nigeria Red Cross. SEMA
US To 'Deepen' Ties With Hotly Courted Africa
Coe Says Kenya Faces 'long Journey' To Tackle Athletics Doping
Spain Busts Criminals Smuggling E-Waste To Africa
US Authorities Search Biden Beach Home In Delaware – Lawyer
Obi Campaigns In Sokoto, Recounts First Encounter With Sultan
I Never Said I'll Announce My Presidential Candidate On Camera, Says Wike


text:
Roller joins Bank of Ireland's US Media Finance team
By Wendy Mitchell2007-10-18T12:46:00+01:00
Michele Roller has joined the Bank of Ireland's US Media Finance team as Director.
She will focus on debt funding solutions for companies in the media sector, offering senior and mezzanine financing to publishing, information services, broadcasting, out-of-home, cable television and digital media companies.
Roller previously served as vice president at Merrill Lynch Capital, and is a veteran of CIBC World Markets, Banque Paribas and MTV Networks.
She will now be based at Bank of Ireland's office in Stamford, Connecticut.
Chris Mangan, head of US Media Finance at the Bank, said: 'We are delighted that Michele has joined the team. We continue to see high levels of activity in the media sector in the US and are confident that Michele's expertise will add considerable weight to our growth ambitions in this sector.'
What plans does director John Cooper have for his Sundance Film Festival farewell?
Cooper has run the Utah-based event for a decade.
'Little Women' producer Amy Pascal: "The world is still really hard on ambitious women"
Greta Gerwig's film is nominated for six Oscars including best film.


text:
2013: The year's biggest Medical Device stories
Canada's pharmaceutical company Valeant Pharmaceuticals entered into a definitive agreement to acquire Bausch + Lomb Holdings for $8.7bn, while Abbott obtained US Food and Drug Administration (FDA) approval for its first-in-class, catheter-based MitraClip therapy. Medicaldevice-network.com wraps-up the key headlines from 2013.
Jarvik Heart obtains regulatory clearance for Jarvik 2000 Heart in Japan
Jarvik Heart obtained regulatory approval for its Jarvik 2000 Heart, a left ventricular assist device (LVAD) implanted inside the heart, from the Japanese Pharmaceuticals and Medical Devices Agency (PMDA).
On November 22, Shonin approval was granted based on Japanese and US clinical trial data of the device, approving it as a bridge to transplant. Century Medical will be the exclusive distributor of Jarvik 2000 in Japan.
At only 30cc volume and 90g (about the size of a C-cell battery), the Jarvik 2000 can support the hearts of patients only 4ft 6in tall and weighing less than 85lb.
Grifols to buy Novartis's diagnostics business for $1.7bn
Spanish company Grifols announced a definitive agreement to acquire Novartis's blood transfusion diagnostics unit for $1.675bn.
The transaction will give critical mass and a significant US presence to Grifols's previously small diagnostics business, which in future is expected to account for more than 20% of the company's sales, compared with the current 4%.
The deal will be structured through Grifols's diagnostic division and a newly created 100% Grifols-owned subsidiary. It will also see an increase in Grifols' workforce by approximately 550 employees as former Novartis employees are likely to be retained.
Abbott obtains FDA approval for first-in-class MitraClip device
Abbott obtained US Food and Drug Administration (FDA) approval for its first-in-class, catheter-based MitraClip therapy, designed for patients with significant mitral regurgitation (MR) who have no surgical treatment options to improve their quality of life.
With FDA approval, the MitraClip device will be immediately available in the US, providing physicians with a new treatment option for patients with significant symptomatic degenerative MR who are at prohibitive risk for mitral valve surgery.
MR is a condition involving the mitral valve of the heart no longer functioning properly, leading to a backward flow of blood.
Stryker to acquire Mako Surgical for $1.65bn
US medical equipment manufacturer Stryker signed a definitive agreement to acquire Mako Surgical, developer of robotic solution and implants for minimally invasive orthopaedic procedures, for $30 per share with an aggregate purchase price of approximately $1.65bn.
Mako Surgical markets joint-specific applications for the knee and hip, proprietary Restoris implants for orthopaedic procedures called MAKOplasty, and the Rio robotic arm interactive orthopaedic system.
The Rio is a surgeon-interactive tactile surgical platform that incorporates a robotic arm and patient-specific visualisation technology, which enables precise, consistently reproducible bone resection for the accurate insertion and alignment of Mako's Restoris implants.
Given Imaging's PillCam SB 3 video capsule receives 510(k) clearance
Israel-based medical devices developer Given Imaging received clearance from the US regulatory agency to market its next-generation video capsule PillCam SB 3, which can detect small bowel abnormalities related to Crohn's disease and gastrointestinal bleeding.
Weighing less than 4g, the 11mm by 26mm video capsule features an imaging device and a light source that can transmit anywhere between two and six images in one second.
Due to software enhancements, the PillCam SB 3 offers a 40% more efficient video compilation than the earlier SB 2 version. The SB 3 will be available in the US towards the end of the year.
Scientists develop intelligent surgical knife to detect cancerous tissue
Researchers at Imperial College London in the UK created an intelligent knife, which can help surgeons identify cancerous tissue.
The iKnife is based on an electrosurgery technology invented in the 1920s and widely used in the medical field today.
With the help of an electrical current, an electrosurgical knife can quickly heat the tissue and then cut through it, while simultaneously reducing the loss of blood.
UK's MHRA to regulate electronic cigarettes as medicines
The UK's Medicines and Healthcare products Regulatory Agency (MHRA) is to regulate all products containing nicotine, including electronic cigarettes, as medicines to ensure product safety and reduce the negative effects of smoking.
The regulation follows concerns that an increase in the popularity of e-cigarettes had led to unregulated and potentially unsafe products being sold.
In England, smoking is the largest single cause of avoidable death every year, killing 80,000 people, and it is hoped that making safe and effective products available for smokers will reduce this risk.
Valeant to acquire Bausch + Lomb for $8.7bn
Canada's leading pharmaceutical company Valeant Pharmaceuticals entered into a definitive agreement to acquire Bausch + Lomb Holdings, a US-based supplier of eye health products, for $8.7bn.
Bausch + Lomb provides vision care products such as contact lenses and solutions; surgical devices such as intraocular lenses and surgical equipment and pharmaceutical drugs such as prescription brands, generics and over-the-counter (OTC) drugs.
The acquisition is a part of Valeant's strategy to take advantage of the growing demand for eye health products, which is driven by an aging patient population, an increased rate of diabetes and demand from emerging markets.
St Jude wins CE mark for deep brain stimulation systems
The European Union granted CE mark certification to St Jude Medical's deep brain stimulation (DBS) systems, designed for the treatment of primary and secondary dystonia, a neurological movement disorder.
The devices, Brio, Libra and LibraXP, are placed using two thin leads with small electrodes that are connected to a power source placed just under the skin near the pectoral muscle in the chest.
The DBS therapy delivers a constant series of painless electrical pulses to the specific target regions including the subthalamic nucleus (STN) or the globus pallidus interna (GPi) of the brain, which are involved with controlling movement.
Medtronic wins CE mark for Engager transcatheter aortic valve implant
The European Union granted CE mark approval to Medtronic's new transcatheter aortic valve implantation (TAVI) system with transapical delivery catheter, for the treatment of patients with severe aortic stenosis who are at high or extreme risk of surgical aortic valve replacement (SAVR).
Using a minimally-invasive delivery system via a catheter, the Engager TAVI system is inserted in the apex (the lower, pointed end) of the heart to promote annular sealing and minimise paravalvular leak (PVL).
Comprising bovine tissue leaflets and a self-expanding nitinol frame, the valve's control arms simplify implantation, and the supra-annular valve positioning facilitates leaflet coaptation (connections) in non-circular anatomy for optimal hemodynamic performance, according to the company.
Biotronik wins CE mark for Ilesto 7 CD/CRT-D series
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The European Union granted CE mark certification to Biotronik's Ilesto 7 implantable cardioverter-defibrillator / cardiac resynchronisation therapy defibrillator (ICD/CRT-D) series.
The Ilesto 7 series, which the company claims include the world's smallest ICDs, feature the company's ProMRI technology, which enables access to potentially life-saving MR scans.
Lucerne Cantonal Hospital physician Dr Paul Erne said patients often require MR scanning at some point in the future, as they get older and have comorbidities.
Direct Flow Medical wins CE mark for transcatheter aortic heart valve system
The European Union granted CE mark certification to Direct Flow Medical's transcatheter aortic heart valve system, featuring a double-ring design to create a tight and durable seal around the annulus.
Using pressurised saline and contrast solution, the Direct Flow Medical transcatheter aortic heart valve's polymer-based metal-free frame is expanded for placement, assessment and repositioning, according to the company.
The system also eliminates aortic regurgitation and reduces procedural risk by eliminating the need for rapid pacing during deployment and post-dilitation.
The evolution of e-labelling in Europe
The adoption of e-labelling, the inclusion of digital instructions with medical devices, has been very slow in Europe. Medical Device Developments spoke to Dario Pirovano, consultant for industry body Eucomed, about the benefits of e-labelling and whether regulatory reform will increasingly promote its use in Europe.
High Triglyceridemia Global Clinical Trials Review, H2, 2013
Shock Global Clinical Trials Review, H2, 2013
Aneurysm Global Clinical Trials Review, H1, 2013
Arrhythmias Global Clinical Trials Review, H2, 2013
Fatigue Global Clinical Trials Review, H1, 2013
Bausch & Lomb Inc
Mako Surgical Corp
BIOTRONIK ApS
Libra Sp. z o. o.
Abbott GmbH & Co KG
Fertility tech in 2023: At-home, personalisation and accessibility
2022 Medtech M&A activity fails to reach the heady heights of 2021


Facebook's latest update to its news feed algorithm comes down to one key factor: people don't like using Facebook anymore. Starting from next week, the company plans to prioritise posts which "spark engagement" over those from media and brands. That means you'll see more updates from Auntie Sue and pictures of friends you haven't seen in two years – but only if enough people comment on them.
In a Facebook post (of course) announcing the changes, founder and CEO Mark Zuckerberg explained users were getting annoyed at "posts from businesses, brands and media" drowning out personal updates. So Facebook is going to penalise posts which don't make you talk.
The update will prioritise posts that trigger "conversations and meaningful interactions between people". But how will this actually change what you see when you scroll down your feed? Essentially, it means you will see more posts from people that Facebook considers to be your friends and family, and less from public pages and publishers. To do this, Facebook will predict which posts you want to see when you login.
These predictions will aim to boost interaction – with the assumption that you are more likely to like or comment on a post if you know the person. One of the problems Facebook is trying to solve, amongst others, is to stop people being so passive when they use the network. It's hard to gauge where Facebook draws the line between passive consumption and engagement, or what Facebook means when it says it wants to increase the amount of "meaningful interactions". A like or a comment doesn't necessarily mean meaningful – although tagging friends in a #relatable meme most likely doesn't count. Crucially, Facebook will favour live video, which it says generates more engagement.
The move could help to remove some of the spam which plagues our feeds; Facebook has made it clear that posts which ask you to 'like, comment, share' will continue to be demoted down your feed. At the same time, certain brands, businesses and the media organisations may see their reach, video watch time and referral traffic decrease dramatically. This means anyone wanting a slice of Facebook's audience will likely have to change approach, or pay up for promoted posts.
Facebook says content from reputable publishers will still be bumped up feeds, but while a lot of great journalism gets read, it might not receive many comments or shares. Which is just what Facebook is going to penalise.
The rationale behind Facebook's latest algorithmic u-turn is that people aren't enjoying their time on Facebook. And Facebook is, seemingly, blaming publishers. The firm cites research that claims social media interactions with people we care about are good for our wellbeing. The inference: interacting with brands and media companies doesn't have the same impact.
Zuckerberg said he expects that "the time people spend on Facebook and some measures of engagement will go down" – which might not be a bad thing. In 2016, the company revealed that the average user spends 50 minutes per day on its Facebook, Instagram and Messenger platforms. And that's not even counting WhatsApp. Investors are less sure. Shares of Facebook fell four per cent after the announcement.
Despite short-term concerns, Facebook needs to act to ensure it's long-term viability. The network has rightly been criticised for its handling of fake news and the effect filter bubbles are having on public discourse. But just prioritising content that your friends and family are interested in or interact is unlikely to wipe fake news from Facebook. People you know could still share a fake news article, or hold damaging views. Only interacting with people who hold your views will make it much easier to encase yourself in this bubble and have your opinions confirmed and bounced back to you.
Going back to his New Year's Resolutions for Facebook, Zuckerberg talks about wanting time on Facebook to be "well spent". That means less funny viral videos, and more content from people you only pretend to like. In it's hunger for advertising dollars, Facebook has found striking that balance impossibly hard.
Yet young people are leaving the site in favour of other networks in their droves, which is hitting Facebook's growth. Research by eMarketer from 2017 shows that Snapchat has almost as many teens as Facebook, despite being an eighth of its overall size. And while Facebook-owned Instagram is seeing rising and double-digit growth amongst teenagers, that same audience is spending less time on Facebook.
Ultimately, this algorithmic update is an attempt to get people spending more time on Facebook and make the network more profitable. Facebook is future-proofing, regardless of the cost.


I grew up visiting this museum and this particular diorama always scared me to death.
The art of remembering and forgetting.
I would like a lobster dress.
You never know what's in a cornfield.
It's all in the titles.
In case you need a laugh.
"The writer Michael O'Donoghue used to say that the definition of insanity is the length of time it takes for a lie to be uncovered. The shorter the period, the crazier you are. By this standard, our president will be setting a new threshold for that definition."
"People tend to think of activism as angry, and certainly there's a place for movements that express public anger. But activism that's effective doesn't all take place in the key of anger. Some of the most powerful actions to be a part of are those where people are coming together and celebrating community and resistance – anything from dance parties in the street to the Women's March, bringing people together more in the spirit of mutual affirmation."
- From this Rolling Stone interview with L.A. Kauffman.
From Mason Dixon Knitting, a little interview with a wonderful woman in Maine. If nothing else, go look at the pictures. They are stunning.
Also from MDK: some thoughts about the pussyhat and about activism.
Finally someone put into words what I've been wondering about for years.
I miss Iain Banks so much.
I'm sure this will be fine.
Remember when our president talked in complete sentences? Need a little inspiration? Listen to Obama's last interview on Pod Save America.
If only I needed another coffee mug.
Over the holiday break, I started three bead and tassel necklaces from Purl Soho.* Click on that link if you want pics of what the kit and finished product look like. I just snapped some detailed shots, not an overview. Because I'm all artsy and stuff.
It's an easy, zen-like project and satisfying.
"Okay, seriously, what do you think is going to happen in the next four years?
So let's get to it."
-- Scalzi. Read the whole thing here.
Related: Let us all channel our inner Maxine Waters.
New column over at Another Mother Runner, in which I go viral.
Before doing anything else, go read this Michael Cunningham piece about David Bowie and theater that is about so, so, so much more. It is amazing.
Having spent a not insignificant amount of time a not insignificant number of these cities, I am unsurprised by this report.
I can't wait until this is done. Who wants to run with me?
"An underappreciation of the Second Law lures people into seeing every unsolved social problem as a sign that the world is being driven off a cliff. But it is in the very nature of the universe that life has problems. It's better to figure out how to solve them, by applying information and energy to expand our niche of life-enhancing order, than to start a conflagration and hope for the best."
- Steven Pinker, Why Things Fall Apart.
Ann Budd explains it all to you, over at Mason Dixon Knitting.


Table lamp with a cylindrical wire framework that widens into a hemisphere at the top. On it sits a hemispherical plastic shade. This lamp is just one of many that shows Verner Panton talent with colour. He was especially known for creating explosions of colour and people stood in long queues to see his designs. For example, he did a show where everything was orange, it was called the Orangery, and it described Verner Panton like crazy - especially in the Danish press. Wire lamp is a sign of yet another explosion.


text:
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Poland >
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Poznań >
Górka Palace in Poznan
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Górka Palace in Poznan is the municipal residence of great splendour that belonged to a prominent Wielkopolskan family was erected in the years 1544-48 and it replaced old Gothic burgher houses. Built for Andrzej Górka of the Łodzia coat of arms, the governor of Wielkopolska, the palace of rectangular plan consisted of two buildings connected through a gate wing in Klasztorna Street.
The Górkas, starting from the above-mentioned Andrzej, were sympathetic towards the Reformation and so the palace became the centre of Lutheranism. When Stanisław, the last male descendant of the family, died in 1592, the palace was inherited by the Czarnkowskis. Four years later it became the property of the city which sold it to the Benedictine sisters from Chełmno. After the suppression of the order in 1833 the palace housed a grammar school for girls and after 1880 was used as a tenement. It has been rebuilt several times but the walls of the cellars and the ground floor remain virtually intact. Damaged in a fire during World War II, the palace was painstakingly rebuilt after the war and presently houses the Archaeology Museum.
The Renaissance sandstone portal (in Klasztorna Street) is most probably a work by an Italian artist. The frame of pilasters supporting the lintel features an arcade with a semicircular arch. Rich ornamentation includes garlands of laurel leaves, cornucopias, masks, flower motifs, grotesques and the engraved date of 1548. The portal opens onto the inner yard with a tree-wing arcaded gallery whose pillars, presumably a work by Silesian stonemasons, have survived to the present day. The yard, which has been recently covered with a glass roof, features a 3.2 metre-high Egyptian obelisk from around 1200 BC. The Archaeology Museum is one of the oldest of its kind in Poland; it continues the tradition of the Museum of Polish and Slavic Antiquities established in 1857.
Source of description: http://www.poznan.pl/mim/public/turystyka/object.html?id_cz=0&id_klasy=2572&id_obiektu=41232&lhs=chapters&rhs=search&lang=en by	strona Urzędu Miasta Poznania
Inside of Górka Palace in Poznan you can also find: Archaeological Museum in Poznan	,
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Poznań, Wodna 27
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text:
Las elecciones parlamentarias de Finlandia tuvieron lugar el 18 de marzo de 2007. Se renovó por completo el Parlamento unicameral, compuesto por doscientos diputados. Entre las competenencias del Parlamento destaca la elección del primer ministro y causar la dimisión de su gabinete, así como el poder de modificar la Constitución.

Las elecciones fueron ganadas por el Partido del Centro, partido político del primer ministro Matti Vanhanen, obteniendo 51 escaños, lo que supuso una pérdida de cuatro escaños. En segundo lugar quedó el Partido de la Coalición Nacional que consiguió cincuenta escaños, diez más que en las anteriores elecciones. Como tercer partido y 45 escaños se situó el Partido Socialdemócrata. Otros cinco partidos, más un representante de la provincia de Åland, consiguieron representación parlamentaria.

Antecedentes

En las elecciones de 1999 ganó el Partido Socialdemócrata (SDP), obteniendo 51 escaños. Pese a la victoria, perdió doce escaños respecto a las anteriores elecciones mientras que centristas y conservadores ascendían en número de votos, quedando los primeros a tres escaños de los socialdemócratas. De cualquier forma, Paavo Lipponen del SDP continuó como primer ministro en una coalición junto a la Liga Verde. Pese a que ese mismo año los socialdemócratas sufrirían una derrota en las europeas, al año siguiente su candidata, Tarja Halonen, ganaría las elecciones presidenciales en la segunda vuelta ante el líder opositor Esko Aho.

Las elecciones municipales de 2000 dieron la victoria al Partido del Centro, mientras que la coalición gubernamental estuvo a punto de romperse por desacuerdos sobre si aceptar o no un nuevo reactor nuclear. Finalmente, y tras una ajustada aprobación en el Parlamento, se acordó que sería el siguiente gobierno quien decidiría. Antes de las elecciones, el gobierno de Lipponen tuvo que superar una moción de censura presentada por centristas y democristianos. Las elecciones de 2003 dieron la victoria a los centristas, formando un gobierno de coalición liderado por Anneli Jäätteenmäki. La coalición la formaron aparte de los centristas, el Partido Popular Sueco y los socialdemócratas.

Anneli Jäätteenmäki se vio obligada a dimitir el 18 de junio por el escándalo Irakgate, que la implicaba en el uso de datos confidenciales para perjudicar al anterior primer ministro. El socialdemócrata Antti Kalliomäki se hizo cargo del gobierno provisional. Pese al escándalo la coalición se mantuvo unida, proclamándose Matti Vanhanen el 24 de junio como primer ministro. Las elecciones presidenciales de 2006 darían de nuevo la victoria a Tarja Halonen superando en esta ocasión al conservador Sauli Niinistö.

Sistema electoral

El Parlamento está compuesto por 200 diputados, elegidos por cuatro años mediante un sistema de representación proporcional. Los candidatos se presentan en listas de partidos políticos. El voto es directo y secreto, pudiendo votar todos los mayores de 18 años. Para las elecciones el país se divide en varias circunscripciones electorales, exactamente entre 12 y 18 distritos. Concretamente, en esta elección hubo 14 distritos, con un diverso número de diputados cada uno. Aparte, la Isla de Åland forma su propia circunscripción uninominal. Por otro lado, la Constitución de 2000 otorgó un mayor poder al Parlamento, reduciendo las competencias de la presidencia.

Candidaturas

Se presentaron dieciocho partidos políticos, sumando un total de 2004 candidatos. Además se presentaron 21 candidatos independientes. Todos los partidos que participaron en las elecciones de 2003 han vuelto a participar en esta ocasión, aunque algunos han cambiado su denominación, como el Partido de los Trabajadores de Finlandia. Por otro lado, más del 60 % de los candidatos eran hombres, siendo las elecciones que más mujeres se presentaron salvo en las de 1991.

Partidos con presencia en el Parlamento

La gran mayoría de los candidatos fueron de los partidos parlamentarios. Exactamente, 1529, es decir, aproximadamente dos tercios del total. El primer ministro Matti Vanhanen lideró el Partido del Centro, que fue el más votado en las elecciones de 2003. El Partido Socialdemócrata, que ocupó el cargo de primer ministro hasta las anteriores elecciones, fue liderado por el ministro de Hacienda, Eero Heinäluoma. Por otro lado el principal partido opositor, el Partido de la Coalición Nacional, fue encabezado por Jyrki Katainen, líder del partido desde 2004.

Entre los partidos de menos peso parlamentario destaca la Alianza de la Izquierda, creada en 1990 como fusión de la Liga Democrática del Pueblo, un antiguo Partido Comunista y el Partido Comunista de Finlandia (Unidad). Por otra parte el Partido Popular Sueco destaca por formar parte del gobierno de Vanhanen pese a ser la séptima formación en número de votos. Los demás partidos son la Liga Verde, que formaba parte de la coalición de gobierno con los socialdemócratas hasta 2003, los Demócrata Cristianos, denominados hasta 2001 Suomen Kristillinen Liitto y los Verdaderos Finlandeses fundado en 1995.

Otros partidos

El número de candidatos entre los partidos sin presencia parlamentaria es variable. El que más presentó fue el Partido Comunista, 171, y el que menos el Partido de la Responsabilidad Conjunta, que sólo tuvo dos candidatos. Los otros partidos que se presentaron fueron el Partido de los Pensionistas, Partido Independiente, Suomen Kansan Sinivalkoiset, Liberales, Para el Pobre, Partido Comunista de los Trabajadores y el Partido de los Trabajadores.

Campaña electoral

Las encuestas previas otorgaban la victoria al Partido del Centro con el 24 % de los votos, un punto por encima de los socialdemócratas. La Coalición Nacional obtendría el 21 % de los votos. Más lejos quedarían la Alianza de la Izquierda (Finlandia) (10 %) y los Verdes (9 %). El primer ministro Vanhanen afirmó que el nuevo gobierno estaría compuesto de tres o cuatro partidos. De esos, dos estarían entre los tres partidos mayoritarios. Otras encuestas reducían la distancia entre socialdemócratas y conservadores. Por otro lado, el 43 % de la opinión pública preferiría la continuidad del gobierno de coalición entre centristas, socialdemócratas y el Partido Popular Sueco. Sin embargo, antes de las elecciones se consideró probable una coalición de centro-derecha con la Coalición Nacional, sobre todo si estos obtenían buenos resultados. Esta opción fue defendida por analistas como Tuomo Martikainen que aseguró que se preparaba una «sorpresa conservadora».

La campaña electoral fue de bajo perfil, alejada de grandes discusiones aunque en la etapa final ganó intensidad. En general los partidos principales lanzaron sus propuestas para mantener el estado del bienestar. Además, en el último año se inició una controversia por las relaciones amorosas del primer ministro aunque finalmente salió beneficiado ante el electorado.

Resultados

Más de cuatro millones de personas estaban convocadas a las urnas. La participación final se situó en el 67,9 %, votando aproximadamente el 30 % antes del 18 de marzo. La mayor participación se dio en el distrito electoral de Vaasa, con el 71,5 % y la menor en la isla de Aland, donde votó el 57 % del censo.

El resultado de las elecciones dio lugar a un Parlamento con nueve fuerzas políticas, exactamente las mismas que ya ocuparon escaños en las anteriores elecciones. El Partido del Centro obtuvo 51 diputados, la Coalición Nacional 50, el Partido Socialdemócrata 45, la Alianza de la Izquierda 17, la Liga Verde 15, el Partido Popular Sueco 9, los Demócrata Cristianos 7 y los Verdaderos Finlandeses 5. Hay que reseñar que los Demócrata Cristianos quedaron por encima del Partido Popular Sueco en porcentaje de voto por cuatro décimas pese a quedar por detrás en número de escaños. Por otro lado, Nauclér Elisabeth consiguió el escaño correspondiente a Aland representando a la Alianza Borgerlig. Obtuvo más del 85 % de los votos.

En una comparativa con los resultados de las elecciones de 2003, se observa que apenas ha habido modificaciones entre los partidos menores. Los mayores cambios se han producido entre los tres partidos mayoritarios, destacando la subida de diez escaños de la Coalición Nacional. Los otros dos principales partidos, centristas y socialdemócratas, perdieron cuatro y ocho escaños respectivamente. Sin embargo, los centristas experimentaron una subida de tres décimas respecto a los resultados de las elecciones municipales de 2004. En conjunto, la mayor bajada fue la del Partido Socialdemócrata que perdió más del 3 % de apoyo respecto a 2003. En cuanto al resto de partidos con presencia parlamentaria, subieron en escaños la Liga Verde, en uno, y los Verdaderos Finlandeses, en dos. La Unión de Izquierda perdió dos diputados, mientras que los democristianos mantuvieron sus siete asientos.

Ninguno de los partidos que quedaron fuera del Parlamento consiguieron superar el 1 % de los votos. El que mejor resultados obtuvo fue el Partido Comunista, un 0'7 % del total, perdiendo más de 2000 votos desde las anteriores elecciones parlamentarias. Dentro de este grupo, quedaron cerca de los comunistas el Suomen Senioripuolue con un 0'6 % y 1500 votos menos que el anterior partido. El resto de formaciones no superó el 0'1 %. El partido que menos votos recibió fue el Partido de la Responsabilidad Conjunta, con 164.

En un análisis regional, sin contar la isla de Aland, en diez de las catorce circunscripciones quedaron entre los tres primeros lugares los tres primeros partidos. Solo en Oulu, Lapland y Vaasa un partido menor consiguió quedar en segundo lugar, la Unión de Izquierda en las dos primeras circunscripciones y el Partido Popular Sueco en la última. La Liga Verde quedó en tercer lugar en Helsinki. Es destacable que en la capital el Partido Centrista sólo obtuvo un diputado de 21 posibles, aunque recuperó la pérdida con sus resultados en otras regiones como Oulu, donde ganó la mitad de los escaños posibles.

Reacciones y formación de gobierno

Pese a que el Partido del Centro ganó las elecciones, el gobierno de coalición (PC, PSD y PPS) sufrió un voto de castigo perdiendo la mayoría absoluta. En la misma noche electoral se sucedieron las declaraciones de los principales líderes políticos. El primer ministro Matti Vanhanen reaccionó de forma agridulce declarando que esperaba mejores resultados, aunque destacó que su partido seguía siendo el más grande del país, y que era más difícil repetir una victoria que ganar desde la oposición. Jyrki Katainen, líder de la Coalición Nacional, se mostró eufórico, declarándose el gran ganador de las elecciones, afirmando que los votantes se habían decidido por el cambio. Añadió además que sería difícil ignorar a los conservadores. Por otro lado, el líder socialdemócrata, Eero Heinäluoma se limitó a declarar que los resultados no eran satisfactorios. Los resultados de esta última formación fueron considerados una debacle por los medios internacionales.

De igual modo, las declaraciones sobre la formación de gobierno no se hicieron esperar. Vanhanen señaló la misma noche electoral que las conversaciones serían muy difíciles y que la coalición sería más amplia que la actual. Aunque aclaró que no estaría formada de ningún modo por los tres principales partidos porque prefería una oposición fuerte. Los otros dos principales partidos también anunciaron su intención de formar gobierno. El emergente Katainen manifestó que su partido estaría dispuesto a formar una coalición con cualquiera de los otros dos partidos mayoritarios. La secretaria general de los socialdemócratas, Maarit Feldt-Ranta, invitó al Partido del Centro a iniciar las conversaciones para volver a formar gobierno. En general los analistas consideraban inevitable un giro a la derecha que conllevaría la entrada de los conservadores al gobierno y el paso a la oposición del Partido Socialdemócrata. En ese contexto de giro conservador, había dudas sobre qué partido minoritario acompañaría a los dos principales. Se planteó la posibilidad que en vez del Partido Popular Sueco fuera la Liga Verde, que consiguió un diputado más en las elecciones.

Finalmente se hizo realidad el giro a la derecha anunciado por los analistas políticos. Vanhanen continuó siendo primer ministro pero con una nueva coalición conservadora. Lo apoyaban la Coalición Nacional, la Liga Verde y el Partido Popular Sueco. Katainen ocupó el puesto de Viceprimer Ministro. La líder de los verdes, Tarja Cronberg se hizo cargo de la cartera de Trabajo y Stefan Wallin, del PPS, de Cultura y Deportes.

Notas

Referencias

2007
Finlandia
Finlandia en 2007


Hello to a celebrity! Adored on shore, this milky-eyed fame monster also acts like a high-maintenance star far before it's caught. For example, although it hates the sun, the walleye might sashay near the surface of the water while demanding access to a rocky bar: it would much rather be in shaded areas, thank you very much. It's also a greedy eater — trout, pike, bass, perch, and minnows will be on the menu — but only at certain times (early morning and after dark hours are its favorite times to dine). Of course, the star treatment doesn't stop at the water's surface. Special consideration for the walleye's habitat and catch limit regulations on shore add to its celebrity status.
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Outlook is one of the best applications of Microsoft which is used to send or receive emails. Most of the Windows users use this application to send email because it has some advantages which differs it from other application. You can use outlook in offline and online both modes. Its depend on you that which mode you want to work. Work offline means that you have not connected to the internet. Outlook provide the facility to work on Offline mode and the email are saved in a folder and as you change the mode of the outlook as online all emails are sent to the destination. You can check that in which mode you are using the outlook. To do this go to the bottom of the Windows, it will show Working Offline. Unless you connect the internet of off offline mode you can't send or receive email.
If you want to turn off work offline mode in Microsoft outlook 2010 then you can do it easily. Click on Send/Receive and notice that the option "Work Offline" is highlighted or not. If it is highlighted then it means that you are offline so click on this option and remove the highlight. You can also turn off work offline mode in Microsoft Outlook 2013. It is compulsory to turn off work offline mode in Microsoft Outlook 2010 and 2013 or its earlier versions because it protect or restrict to connect to internet and without connecting to internet the email can't be sent or received. The extension of Offline mode is .ost (outlook Data File) which is the copy of Exchange mailbox. When you online this is synchronized automatically and the mail sent to the destination but it depends on your setting because if you have set that the email sent automatically when internet is connected then it will work otherwise in offline mode it can't sent mail.


Adelaides very own Pure vision wines have created the ultimate Rosé bursting with flavours of strawberries, raspberries and ripe apples. This beautifully crafted bottle will add that extra special touch to any pretty bunch.
PLEAS NOTE: This wine is only available on the 13th and 14th on February as it is a Valentines Day special. All orders made for other dates will be refunded.
Due to the large volume of orders we cannot guarantee a specific delivery time, however all flowers and gifts will be delivered between 9am and 5:00pm.


"Pam, Rikki, and Mike are discussing recent policy changes at Walt Disney World when it comes to strollers, smoking areas, and even ice! Also, we take a question from Listener Morgan about our ultimate Walt Disney World resort, restaurant, Fastpass, and more! We hope you enjoy today's show!"


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Archives of the Turkish Society of Cardiology
The impact of addressing modifiable risk factors to reduce the burden of cardiovascular disease in Turkey [Turk Kardiyol Dern Ars]
Turk Kardiyol Dern Ars. Ahead of Print: TKDA-40330 | DOI: 10.5543/tkda.2019.40330
The impact of addressing modifiable risk factors to reduce the burden of cardiovascular disease in Turkey
Yücel Balbay1, Isabelle Gagnon-Arpin2, Simten Malhan3, Mehmet Ergun Öksüz3, Greg Sutherland2, Alexandru Dobrescu2, Guillermo Villa4, Gülnihal Ertuğul5, Mohdhar Habib6
1University of Health Sciences, Ankara Yüksek Ihtisas Training and Research Hospital, Ankara, Turkey
2The Conference Board of Canada, Ottawa, Ontario, Canada
3Sağlık Yönetimi Programı, Baskent University, Ankara, Turkey
4Economic Modeling Center, Global Health Economics, Amgen Economic Modeling Center of Excellence, Zug, Switzerland
5Health Policy and Reimbursement, Amgen, Istanbul, Turkey
6Value, Access and Policy, Amgen, Inc, Thousand Oaks, California, United States of America
Our study aimed to estimate the impact of addressing modifiable risk factors on the future burden of cardiovascular diseases (CVD) in the general population and in two high-risk populations (heterozygous familial hypercholesterolemia and secondary prevention) for Turkey.
One model investigated the impact of reaching World Health Organization (WHO) voluntary targets for tobacco use, hypertension, type 2 diabetes, obesity and physical inactivity in the general population. Another model estimated the impact of reducing LDL-cholesterol in two high-risk populations through increased access to effective treatment. Inputs for the models include disease and risk factor prevalence rates, a population forecast, baseline CVD event rates, and treatment effectiveness, primarily derived from the published literature. Direct costs to the public health care system and indirect costs from lost production are included, although the cost of programs and pharmacological interventions to reduce risk factors was not considered.
The value of reaching WHO risk factor reduction targets is estimated at US$9.3 billion over the next 20 years, while the value of reducing LDL-cholesterol is estimated at up to US$8.1 billion for high-risk secondary prevention patients and US$691 million for heterozygous familial hypercholesterolemia patients.
Efforts to achieve WHO risk factor targets and further lower LDL-cholesterol through increased access to treatment for high-risk patients are projected to greatly reduce the growing clinical and economic burden of CVD in Turkey.
Keywords: Cardiovascular Diseases, Heart Diseases, Risk Factors, Hyperlipidemias, Public Health
Corresponding Author: Yücel Balbay, Türkiye
TOOLS Print Download citation RIS EndNote BibTex Medlars Procite Reference Manager Share with email Share Send email to author Similar articles PubMed Google Scholar


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Каризал Гранде има више значења:

 Каризал Гранде (Сиудад дел Маиз), насеље у савезној држави Сан Луис Потоси у Мексику
 Каризал Гранде (Санта Катарина), насеље у савезној држави Сан Луис Потоси у Мексику
 Каризал Гранде (Закуалпан), насеље у савезној држави Веракруз у Мексику


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Q: Xamarin forms navigation page back button mvvm command Is there a property in XAML to set when back button pressed launch MVVM method through command?
Or another way to do it avoiding code behind?

A: I think you can use EventToCommandBehavior to bind NavigationPage.Popped, NavigationPage.PoppedToRoot events to a Command on XAML.

redpajama_set_name: RedPajamaStackExchange


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from django.db import migrations

def forwards(apps, schema_editor):
    from manabi.apps.flashcards.models import Deck

    for deck in Deck.objects.all().iterator():
        deck.refresh_card_count()

class Migration(migrations.Migration):

    dependencies = [
        ('flashcards', '0054_auto_20171211_0313'),
    ]

    operations = [
        migrations.RunPython(forwards),
    ]


A brand new show from top Scottish comedian Danny Bhoy. All profits from each show will go to locally-based charities working mainly with disadvantaged children and young adults. More details at www.dannybhoy.com. Early booking recommended! 'His routines have the mark of those that are quoted from, for years to come' (Herald). 'One of the most sparkling wits in the world' (Montreal Gazette).
Sorry. We have no upcoming listings for Danny Bhoy: 12 Nights, 12 Charities.