text:
Lufkin Employment Attorneys & Lawyers
Lufkin Attorneys
Nena Chima
Nena has a demonstrated history in the public service sector and professional experience in the areas of civil litigation, corporate law, and government law. She is well-ve... read more
Licensed in TX |
"Nena is great and so easy to work with. No question went unanswered and she was able to explain things in a way that made sense to me. Sp..."
Irma Jacobson
Irma Jacobson is an attorney at law with over five years of experience. She is licensed to practice law in Texas and obtained a Juris Doctorate degree in law after she graduated from the University of Houston. Irma focuses on providing legal assistance to entrepreneurs and start-up companies. She is also skilled in drafting, reviewing and negotiating commercial contracts. Irma has been serving as the founding attorney of her own law firm, Jacobson PLLC, since January 2018.
6 yrs experience | Licensed in TX | verified
"Irma is hardworking and fights for her clients. She is very talented and intelligent and puts her best foot forward for every case. I str..."
Chris Bourgeacq
Chris Bourgeacq is the founder of The Chris Bourgeacq Law Firm, PC in Austin, Texas. Board certified in Labor & Employment Law, he brings three decades of legal experience with extensive expertise in workplace law, labor law, litigation and mediation, and commercial law. In his spare time, Chris works with the Volunteer Legal Services of Central Texas. He was an Honor Scholar at the Washington University School of Law.
31 yrs experience | Licensed in IL, MO, TX |
"BRILLIANT ATTORNEY! HIS EXPERIENCE AND SUCCESS RATE ARE UNPARALLELED If you are reading this review, look no further. You have truly fo..."
John Doe is a corporate attorney that specializes in legal documents. He has extensive experience in research, reviewing and writing a range of different legal documents. John is also experienced in other corporate legal practice areas, including business formation, business operation and employment law. He obtained his law degree from the Oklahoma City University School of Law and soon after receiving a license to practice law in Texas. John currently operates his own law office.
Gary Kissiah
Gary's focus has been on technology and corporate law for two decades. He was a partner in the Austin office of Akin Gump and was co-chair of the Technology Practice Group.... read more
40 yrs experience | Licensed in TX | verified
"Gary is that rare attorney who deeply understands both corporate and technology law and business strategy. I was fortunate to have worked..."
Brian Hall
Brian A. Hall is a member of Traverse Legal, PLC and the managing partner of its Austin, Texas office. He has represented enterprise Internet and technology companies, star... read more
12 yrs experience | Licensed in MI, TX | verified
"Before Brian, we would regularly have to pay attorneys to learn our business, research legal issues for the first time and provide advice..."
Brandy Austin
Brandy Austin worked hard throughout school and completed the Baylor's prestigious practice court program. She has over 7 years of experience in the field and is a well-known, aggressive attorney who does not take no for an answer. With experience in bankruptcy, elder law, family law, personal injury, and business litigation, you can count on Brandy to handle any matter that you present to her.
Consultant Statement of Work
Transportation Services Agreement
Confidentiality and Invention Assignment Agreement
What is a Statutory Close Corporation
Model Release Form
Disposable Income: Everything You Need to Know
What Is a Pet Purchase Contract?
What Is the Highest Position in a Company?
FMLA Texas: Everything You Need to Know
FMLA Intermittent Leave: Everything You Need to Know
More Lufkin Attorneys
Lufkin Patent Attorneys
Lufkin Trademark Attorneys
Lufkin Contract Attorneys
Lufkin Immigration Attorneys
Lufkin Business Attorneys
Gilmer Employment Attorneys
Kilgore Employment Attorneys
Longview Employment Attorneys
Marshall Employment Attorneys
Nacogdoches Employment Attorneys
Lufkin Employment Lawyers
Why use UpCounsel to hire a Lufkin Employment Attorney?
Legal Services Offered by Our On-Demand Lufkin Employment Attorneys
Our experienced Lufkin employment attorneys & lawyers can help guide you on how to proceed with various employee decisions such as reviewing employee documents such as contracts, agreements, policies, and handbooks, along with difficult decisions such as firing, lawsuits, claims, and complaints.
Although not every single employment contract will require legal assistance, many employment lawyers would recommend avoiding unilateral employment contracts that strongly benefit one side over the other. These types of employee contracts rarely hold up in court, yet having the funds needed to combat an issue in court can limit the employee's options.
A confidentiality agreement and a non-compete agreement are common forms of employee contracts that one of our Lufkin employment attorneys can help customize for your business. If your business needs to fire an employee, proper measures should be taken from a business legal standpoint to ensure proper communication and a smooth transition of dismissing that employee. In any case, we suggest you connect with our employment attorneys to discuss your options.
If You Need Ongoing Legal Counsel or Ad-hoc Legal Work - We Can Help!
Improve Your Legal ROI with Affordable Employment Attorneys that service Lufkin, TX.
Want to Connect with Top Lufkin Employment Attorneys & Lawyers?


text:
Q: Explain Analyze Explained in PostgreSQL If I'm running  the following query on PostgreSQL:
select *
from osm_pois_v06 pp
where pp.geom && ST_MakeEnvelope(8.174,48.298,12.431,50.930,4326);

I have to wait 1,34 minutes.
But if I'm doing an execution plan (explain analyze) with the mentioned query, then I have the following output:

The plan tells me that the  execution time is 2,624 seconds. But why is it less than 1,34 minutes?

A: Because you are using pgAdmin to fetch a result set of over 600000 rows.
pgAdmin is known to be slow when displaying large result sets. You'd be better off with psql.

A: The easiest way to estimate the impact of slow client software and/or network connection on you query execution is to add 'create temp table as ' before your select. If that runs in 4 seconds, and 1:34 without the 'create temp' part, it is a safe bet that the bottleneck is in transferring the result set out of the DB and processing it on the receiving end.

redpajama_set_name: RedPajamaStackExchange


Oriental Inspection & Service Co.,Ltd.
Good day and Happy new year !
Kindly noted that our office will be closed from 02/FEB/2019 (SAT) until 06/FEB/2019 (WED) due to Korea's holiday : Lunar New Year's Day.
If you have any inquiries during the holiday, please send us e-mail.
Thank you for your kind understanding in advance.


text:
\section{Introduction}
\IEEEPARstart{N}{owadays}, \textit{Model Predictive Control} (MPC) has become the most popular advanced control technology for several complex engineering applications \cite{borrelli2017predictive}. Apart from computational aspects, it is widely recognized that one key practical challenge in MPC arises when dealing with uncertainty, especially when the prediction model is identified using open-loop data taken from a specific operation of the plant~\cite{mesbah2016stochastic}.

In case of partially known systems, traditional MPC approaches exhibit some degree of robustness, so that marginal robust performance can be guaranteed. When intrinsic robustness of deterministic
MPC is not enough, robust MPC \cite{falugi2014getting} and stochastic MPC  \cite{mesbah2016stochastic} approaches have been developed to take into account  uncertainties. However, regardless of the specific technique, increasing robustness of the MPC controller usually leads to   conservative performance \cite{bemporad1999robust}.

While there is usually a separation between model identification and control design, an alternative approach to managing uncertainty in designing control systems is to revisit the identification process as a procedure \textit{to be designed} by bearing the final control application in mind. Such a rationale is known as \textit{Identification for Control} (I4C) and has been widely studied for fixed-order (oftentimes, PID) control of \emph{Linear Time-Invariant} (LTI) systems \cite{gevers2005identification}. According to I4C, the best model for control may \textit{not} be the one providing the least output prediction errors, but the one providing the best performance on the true system when in closed loop with the associated model-based controller.

As far as we are aware of, the I4C modeling approach has \textit{never} been applied to MPC control. Learning techniques have instead been shown to be useful for iterative MPC tasks in \cite{rosolia2018learning} and in reinforcement learning applied to MPC~\cite{ZGB19}. Furthermore, data-driven approaches have been proposed for direct MPC optimization using open- and closed-loop data, see, e.g., \cite{kadali2003data,PiFoBe2017}. Although the above approaches are powerful tools for control design in case of unknown systems, they fail to provide a mathematical (albeit control-oriented) description of the plant. Indeed, the latter can often be   useful for physical interpretation, performance monitoring, and diagnosis \cite{schafer2004multivariable}.

In this work, we propose an \textit{Identification for (Model-Predictive) Control} approach aimed at finding the best prediction model for MPC  from experimental data, by considering the control objective directly in the model learning phase. We propose a  hierarchical architecture, typically employed in several industrial applications, in which the inner controller is a parametric filter (e.g., a PID controller) aimed to stabilize the system at a fast pace, whereas the outer loop plays the role of a reference governor (RG)~\cite{garone2017reference,BCM97} with a twofold goal: ($i$) boosting the performance of the inner loop and ($ii$) handling the signals constraints due, e.g., to actuator bounds or system limitations. Within this framework, the RG is typically an MPC law based on a model of the inner loop. According to the I4C philosophy, we propose a change of perspective and treat such a model as a \textit{design parameter} instead. Such a parameter will be iteratively optimized, together with the inner controller, using closed-loop data collected on the plant and Bayesian optimization. Finally, we show that, using the same rationale and tools, also
the prediction horizon, a critical parameter to tune in MPC, can be optimized from data.

For the sake of completeness, the first use of Bayesian optimization in control-oriented identification was proposed in \cite{bansal2017goal}, based on a simpler control scheme. The same hierarchical architecture was instead addressed in \cite{PiFoBe2017} to design the controller from data, but without providing an MPC-oriented model of the plant.

The remainder of the paper is as follows. In Section \ref{sec:problem} the control problem of interest is formally stated. The hierarchical architecture is introduced in Section \ref{sec:architecture}, where also the parameterization of each block is described (and motivated) in detail. The proposed strategy is described in Section \ref{sec:main}, where a discussion on how to practically restrict the parameter space is also provided. Section \ref{sec:example} illustrates the performance of the method on a benchmark   example.

\section{Problem formulation}\label{sec:problem}

Consider a \emph{multi-input multi-output} (MIMO) plant $\mathcal{S}$, with input $u\in \mathbb{R}^{n_u}$ and output $y\in \mathbb{R}^{n_y}$ signals sampled at a regular time interval $T_s$. We aim at synthesizing a controller $\mathcal{C}$ for $\mathcal{S}$
such that the controlled closed-loop system achieves a desired engineering objective defined in terms of minimization of a cost $J(y_{1:T},u_{1:T})$, where $y_{1:T}$ (resp. $u_{1:T}$) denotes the sequence of output  (resp. input) signals measured at time steps $t=1,\ldots,T$, and $T$ is the length (measured in number of samples) of the experiment where the closed-loop performance is measured. Besides minimizing the cost $J(y_{1:T},u_{1:T})$,
the following constraints on inputs and outputs should be satisfied:
\begin{subequations} \label{eq:constraints}
	\begin{align}
	u_{\mathrm{min}} &\leq u(t) \leq u_{\mathrm{max}}, \label{eq:constraintsJa}\\
	\Delta u_{\mathrm{min}} &\leq u(t)-u(t-1)  \leq \Delta u_{\mathrm{max}}, \label{eq:constraintsJb}\\
	y_{\mathrm{min}} &\leq y(t)  \leq y_{\mathrm{max}}, \quad  t=1,\ldots,T. \label{eq:constraintsJc}
	\end{align}
\end{subequations}
Constraints~\eqref{eq:constraints} are generally imposed by actuator limitations or might reflect safety conditions. The control design problem is formulated as the following optimization problem:
\begin{align} \label{eqn:costCLv1}
\min_{\mathcal{C} \in \mathcal{\mathbf{C}}}& \quad J(y_{1:T},u_{1:T}) \quad
\textrm{s.t.}   \quad \text{(\ref{eq:constraints})},
\end{align}
with $\mathcal{\mathbf{C}}$ denoting the set of controller candidates.

We rewrite constraints~(\ref{eq:constraints}) as $h_i(t) \geq 0$, $i=1,\ldots,6$,
with
\begin{subequations} \label{eqn:poscons}
	\begin{align}
	&  h_1(t)= u(t) - u_{\mathrm{min}}, \ \ \ \ \  h_2(t)=   u_{\mathrm{max}}- u(t),\\
	&  h_3(t)= u(t)-u(t-1) - \Delta u_{\mathrm{min}} \geq 0,\\
	&  h_4(t)= \Delta u_{\mathrm{max}} -  u(t)+u(t-1)\geq 0 ,\\
	&  h_5(t)= y(t) - y_{\mathrm{min}}, \ \ \  h_6(t)=   y_{\mathrm{max}}- y(t),
	\end{align}
\end{subequations}
and treat them with penalty functions
\begin{align} \label{eqn:costCL}
\min_{\mathcal{C} \in \mathcal{\mathbf{C}}}& \quad \tilde{J}(y_{1:T},u_{1:T},\mathcal{C} )
\end{align}
where
\begin{align}
\tilde{J}(y_{1:T},u_{1:T}) = J(y_{1:T},u_{1:T}) + \sum_{t=1}^{T}\sum_{i=1}^6b_t(h_i(t))
\label{eq:finalJC}
\end{align}
and $b_t:\mathbb{R}\to \mathbb{R}$ are (possibly time-varying) barrier functions.

Assuming zero initial conditions, clearly $y_{1:T}$, $u_{1:T}$ in~\eqref{eq:finalJC} are only functions of the controller $\mathcal{C}$ and of the process model $\mathcal{S}$. Rather than first fixing a model for $\mathcal{S}$ (either from  first-principle physical laws or using system identification techniques), we follow a \emph{performance-driven} control design paradigm
and leave the model of $\mathcal{S}$ as a degree of freedom, used to
minimize the closed-loop cost $\tilde{J}(y_{1:T},u_{1:T})$.

\section{Control architecture}\label{sec:architecture}

We adopt the hierarchical, multi-rate, reference-governor control architecture  in  Fig.~\ref{fig:hierarchical_outinner}, consisting of:
\begin{itemize}
	\item an inner low-level  controller   $\mathcal{K}$
	which operates at sampling time $T_s$ and  it is
	mainly used to handle fast
	dynamics of the system. This controller introduces a
	degree-of-freedom in the control design and, in case
	of unstable plants $\mathcal{S}$, it might also stabilize the inner
	closed-loop system $\mathcal{M}$. Nevertheless, the latter is not a
	required condition in our design approach.

 \item an outer MPC to enhance performance of the inner loop $\mathcal{M}$ an to enforce constraints~\eqref{eq:constraintsJc}. The MPC operates at a sampling time $T_{\text{MPC}}$ that is an integer multiple of $T_s$, \emph{i.e.}, $T_{\text{MPC}}=NT_s$ with $N   \in\mathbb{N}$.
Setting $T_{\text{MPC}}$ larger than $T_s$ (thus, $N > 1$) may be needed  to solve the constrained optimization problem on line, \emph{i.e.}, within the MPC sampling time $T_{\text{MPC}}$.
\end{itemize}
In standard RG approaches, the outer MPC requires a prediction model of the inner loop $\mathcal{M}$.
In accordance with the \emph{performance-driven}  approach proposed in this paper,
we treat such a model as a \textit{design parameter}
and look for the model providing the best closed-loop performance according to the performance index $\tilde{J}(y_{1:T},u_{1:T})$.

In particular, as detailed in the following, a model of the plant $\mathcal{S}$ will be used neither to design the controllers  nor to evaluate  the  performance index $\tilde{J}(y_{1:T},u_{1:T})$, which will be instead measured directly from closed-loop experiments performed on the actual plant.

\begin{figure}[!b]
	\begin{tikzpicture}[scale=.77]

	\node [draw, rectangle,minimum width=1.2cm,minimum height=1cm,thick] (Gp)  at (4,1.5) {$\mathcal{S}$};

	\node [above] (yo)  at (5.2,1.5) {};
	\node [draw, rectangle,minimum width=1.2cm,minimum height=1cm, thick] (Kp)  at (1.5,1.5) {$\mathcal{K}$};

	\draw[->] [thick]   (Kp) -- (Gp);
	\node [] (plus)  at (6.9,1.5) {};
	\node [draw, circle,scale=0.5] (pluserr)  at (0,1.5) {};
	\node [above right] (r)  at (-1.5,1.5) {\footnotesize $g(t)$};

	\node [below left,scale=0.5]    at (0,1.35) {$-$};
	\draw[->] [thick] (Gp) -- (plus);

	\node[above] (ynode22) at  (6.5,1.5) {\footnotesize  $y(t)$};
	\draw[->] [thick]  (6.0,1.5) -- (6.0,-0.8) -- (-3.3,-0.8) -- (-3.3, 1.2) -- (-2.95,1.2);

	\draw[->] [thick] (pluserr) -- (Kp);
	\node [above] (u)  at (2.75,1.5) {\footnotesize $u(t)$};
	\node [above] (e)  at (0.4,1.5) {\footnotesize $e(t)$};
	\draw[<-] [thick] (pluserr) -- (0,0.5) -- (5.3,0.5) -- (5.3,1.5);

	\draw[dashed] (-0.65,-0.3) rectangle(5.6,2.5);
	\node [above right] (M)  at (-0.7,-0.25) {$\mathcal{M}$};
	\node [draw, rectangle,minimum width=1.2cm,minimum height=1cm, thick] (MPC)  at (-2.2,1.5) {$\mathrm{MPC}$};

	\draw[->] [thick] (MPC) -- (pluserr);
	\node [left] (rout)  at (-3.8,1.5) {};
	\node [above] (rout2)  at (-3.5,1.5) {\footnotesize $  r(t)$};
	\draw[->] [thick] (rout)--(MPC);
	\end{tikzpicture}

	\caption{Proposed hierarchical control architecture. $\mathcal{S}$: plant to be controlled; $\mathcal{K}$: inner  controller; $\mathcal{M}$: inner closed-loop system; $r(t)$:   reference to be tracked.}  \label{fig:hierarchical_outinner}  \vspace*{-0.0cm}
\end{figure}

\subsection{Inner controller parameterization} \label{Sec:inner}
The inner controller $\mathcal{K}$ is parameterized by a vector $\theta\in\mathbb{R}^{n_\theta}$. For instance, $\mathcal{K}$ can be a simple discrete-time \emph{proportional-integral-derivative} (PID) controller, with sampling time $T_s$ and discrete-time transfer function
\begin{equation}
\label{eq:PID}
\mathcal{K}(z,\theta) = \theta_P + \theta_IT_s\frac{1}{z-1} + \theta_D \frac{N_d}{1+N_dT_s\frac{1}{z-1}},
\end{equation}
where $\theta = \left[ \theta_P \ \  \theta_I\ \ \theta_D \right]'$ is the design parameter vector   and  $N_d \gg 1$  limits the high-frequency gain of the PID controller. Although $N_d$ may  be treated as a design parameter, its tuning  is generally not critical and thus   not included in $\theta$.

\subsection{Outer MPC parameterization} \label{Sec:outerMPC}
The most important component of the outer MPC is the model
used to predict the output $y$ and input $u$ as a function of the MPC command $g$.
Let $M$ be the dynamical model from $g$ to $\smallmat{y\\u}$, described in the  state-space representation \begin{equation}
\left\{\begin{array}{rcl}
\xi(t+1)&=&A_{M}\xi(t)+B_{M}g(t)\\
\matrice{c}{y(t)\\u(t)}&=&C_{M}\xi(t)+D_{M}g(t),
\end{array}
\right.
\label{eqn:SITO}
\end{equation}
where $\xi\in\mathcal{R}^{n_\xi}$ is the state of the closed-loop model.
For instance, in the case of a single-input-single-output  plant,
the $2\times1$ transfer matrix $M$ can be modelled as a pair of
transfer functions with the same  poles.
Let $\mu\in\mathbb{R}^{n_\mu}$ be the vector obtained by stacking the entries of  $A_{M}, B_{M}, C_{M}$ and $D_{M}$.

At each time instant $t$ integer multiple of the MPC sampling time $T_{\text{MPC}}$ (\emph{i.e.}, $t=hT_{\text{MPC}}$ with $h \in \mathbb{N}$), the outer MPC solves the  minimization problem
\begin{subequations} \label{eq:MPC}
	\begin{align}
	&  \nonumber \min_{\left\{g(t+k|t)\right\}_{k=1}^{N_u}, \epsilon} Q_y \!\sum_{k=1}^{N_p} \left(y(t+k|t)-r(t+k)\right)^2+\\
	&  \nonumber \quad\quad\quad\quad+ Q_u\! \sum_{k=1}^{N_p} \left(u(t+k|t)-u_{\rm ref}(t+k)\right)^2 \!+\!\\
	&   \quad\quad\quad\quad + Q_{\Delta u}\!\sum_{k=1}^{N_p}\!\!\left(\!u(t+k|k)\!-\!u(t+k-1|t)\right)^2   \!\!+\! Q_\epsilon \epsilon^2\\%+ Q_{g}\sum_{k=1}^{N_u}

	&  \mathrm{s.t. \ }    \matrice{c}{y(t+k|t)\\u(t+k|t)} = M\left(\mu,
	g(t+k|t)\right),  \ \   k=1,\ldots,N_p  \\
	& \quad y_{\mathrm{min}} \!-\! V_y\epsilon\leq y(t+k|t) \leq  y_{\mathrm{max}}+V_y\epsilon, \  k=1,\ldots,N_p  \\
	& \quad   u_{\mathrm{min}}\!-\!V_u\epsilon \leq u(t+k|t) \leq  u_{\mathrm{max}}+V_u\epsilon, \  k=1,\ldots,N_p \\
	& \quad  \Delta u_{\mathrm{min}} -V_{\Delta u}\epsilon \leq \Delta u(t+k|t), \ \    k=1,\ldots,N_p \label{eq:MPC:Ducons1}  \\
	& \quad    \Delta u(t+k|t) \leq  \Delta u_{\mathrm{max}}+V_{\Delta u}\epsilon, \ \  k=1,\ldots,N_p \label{eq:MPC:Ducons2}\\
	& \quad  g(t+N_u+j|t)= g(t+N_u|t),  \ \  j=1,\ldots, N_p-N_u	 \label{eqn:MPCconst}
	\end{align}
\end{subequations}
where $\Delta u(t+k|t) =  u(t+k|t)- u(t+k-1|t)$,   $N_p$ and $N_u$ are the prediction and control horizon, respectively, $Q_{y}$, $Q_{u}$, $Q_{\Delta u}$, $Q_\epsilon$ are nonnegative weights, $u_{\rm ref}$ and $r$ are the input and output references, respectively,  $V_y$, $V_u$, $V_{\Delta u}$ are positive vectors that are used to soften the  constraints on plant's input and output, so that~\eqref{eq:MPC} always admits a solution. According to standard MPC design, in case $N_u<N_p$,  the constraint \eqref{eqn:MPCconst} enforces a constant value of   $g$ from time $N_u$ to $N_p$.
The reader is referred to \cite{borrelli2017predictive} for an overview on  MPC design.

We can also treat the
prediction horizon $N_p$ as a design parameter, and denote by
$\nu=\left[\mu'\ \ N_p \right]'$, $\nu\in\mathbb{R}^{n_\mu}\times\mathbb{N}$ the overall vector of tuning parameters. The control horizon $N_u$ determines, together with the number of constraints in~\eqref{eq:MPC}, the computational complexity of the outer MPC controller. Therefore, it is usually fixed by the available online throughput. Alternatively, we can set $N_u=N_p$.

The remaining MPC parameters ($\Nu$, $Q_{y}$, $Q_{u}$, $Q_{\Delta u}$, $Q_\epsilon$, $V_y$, $V_u$ and $V_{\Delta u}$) are   treated as a specification
of the desired closed-loop performance, and therefore not optimized.
More generally, we could decouple the MPC quadratic cost in~\eqref{eq:MPC} from
the closed-loop performance index $\tilde{J}(y_{1:T},u_{1:T})$. For instance, $\tilde{J}(y_{1:T},u_{1:T})$ can be a general, possibly non-convex function
reflecting engineering or economic goals,  while the cost of the MPC~\eqref{eq:MPC} is  quadratic to facilitate online optimization. Indeed, in case the augmented model $M$ is LTI as in~\eqref{eqn:SITO},  problem~\eqref{eq:MPC} reduces to a  \emph{quadratic programming} (QP) problem whose solution can be   computed both \emph{offline} using multiparametric quadratic programming~\cite{BEMPORAD20023} or online using dedicated QP solvers based, \emph{e.g.}, on interior-point algorithms~\cite{WaBo10}, fast gradient projection~\cite{PaBe14}, or active set methods~\cite{ferreau2008online}.

\section{Performance-driven parameter tuning}\label{sec:main}

Based on the controller parametrization introduced in the previous section, the closed-loop performance cost $\tilde{J}(y_{1:T},u_{1:T})$ is  a function of vectors $\theta$ and $\nu$ parametrizing the inner controller $\mathcal{K}$ and the outer MPC, respectively. Thus, under the hierarchical architecture of Fig.~\ref{fig:hierarchical_outinner}, the original control design problem~\eqref{eqn:costCL} is equivalent to
\begin{align} \label{eqn:costCLtot}
\min_{\theta,\nu}& \quad \tilde{J}(y_{1:T},u_{1:T};\theta,\nu).
\end{align}

\subsection{Bayesian optimization for parameter selection}

\begin{algorithm}[!b]
	\caption{Bayesian optimization for controller design}
	\label{algo:BO}
	%
	\begin{enumerate}[label=\arabic*., ref=\theenumi{}]
		\item \label{algo:BOv1} \textbf{initialize} parameters \emph{vs} performance set
		\begin{align*} \mathcal{D} \leftarrow \{(\theta_{1:N_{\text{in}}},\nu_{1:N_{\text{in}}}),\tilde{J}_{1:N_{\text{in}}}\};
		\end{align*}
		\item  \textbf{for} $i=N_{\text{in}},\ldots,i_{\mathrm{max}}-1$ \textbf{do}
		\begin{enumerate}[label=\theenumi{}.\arabic*., ref=\theenumi{}.\theenumii{}]
			\item \label{algo:BOv2} based on the data  $\mathcal{D}$,  \textbf{train} a GP  approximating $\tilde{J}$;
			\item \label{algo:BOv2.2} based on the GP,  \textbf{define} acquisition function $\alpha(\theta,\nu|\mathcal{D})$;
			\item \textbf{compute} next controller parameters $\theta_{i},\nu_{i}$ as
			\begin{align*}
			\theta_{i+1},\nu_{i+1} \leftarrow \arg\max\limits_{\theta,\nu}\alpha(\theta,\nu|\mathcal{D});
			\end{align*}
			\item \label{algo:BOv3} \textbf{perform} closed-loop experiment and \textbf{measure} performance index $\tilde{J}_{i+1}$;
			\item \textbf{augment} the training set {\small $ \mathcal{D}\! \leftarrow \! \mathcal{D} \cup \{(\theta_{i+1},\nu_{i+1}),\tilde{J}_{i+1}\}$};

			\item   \textbf{exit for loop} if a termination criterion is met;
		\end{enumerate}
		\item \textbf{end for}
		\item \textbf{compute} optimal  parameters as $\theta_{i^{\star}}$ and $\nu_{i^{\star}}$, with
		\begin{align*}
		i^{\star}=\arg \max\limits_{i}\tilde{J}_{i};
		\end{align*}
	\end{enumerate}
	\vspace*{-.0cm}\hrule\vspace*{.0cm}
	~~\textbf{Output}:  Optimal controller parameters $\theta_{i^{\star}}$ and $\nu_{i^{\star}}$.
\end{algorithm}

The design problem~\eqref{eqn:costCLtot} is solved through the \emph{Bayesian optimization} (BO) strategy \cite{shahriari2016taking} outlined in Algorithm~\ref{algo:BO}. The algorithm is initialized (step~\ref{algo:BOv1}) by  performing $N_{\text{in}} \geq 1$ closed-loop experiments for $N_{\text{in}}$ different (e.g., randomly chosen) values of controller parameters $\theta_i$ and $\nu_i$ (with $i=1,\ldots,N_{\text{in}}$). For each pair $(\theta_i,\nu_i)$, a closed-loop experiment is performed and the performance index $\tilde{J}_i$ is measured. In this way,  an initial set $\mathcal{D} = \{(\theta_{1:N_{\text{in}}},\nu_{1:N_{\text{in}}}),\tilde{J}_{1:N_{\text{in}}}\}$ of parameters and corresponding performance $\tilde{J}$ is constructed, with $\theta_{1:N_{\text{in}}}$ denoting the sequence $\theta_{i}$ for $i=1,\ldots,N_{\text{in}}$. In practice, the experiment can be interrupted and large cost assigned to $\tilde{J}_i$  in case of safety constraint violations.

The algorithm is then iterated until a stopping criterion is met (e.g., maximum number of iterations reached). At each iteration $i \geq N_{\text{in}}$, the following two steps are performed:
\begin{itemize}
	\item \emph{Learning phase} (Step~\ref{algo:BOv2}). In this step, a Gaussian Process (GP) describing our ``best guess'' of the cost $\tilde{J}(y_{1:T},u_{1:T};\theta,\nu)$
corresponding to the design parameters $\theta$ and $\nu$ is fitted on the available data  $\mathcal{D}$.

	Under the prior assumption that the   cost $\tilde{J}$ is generated by a GP with zero mean and
	covariance function $\kappa((\theta,\nu),(\tilde \theta,\tilde \nu))$, the posterior distribution of    $\tilde{J}(y_{1:T},u_{1:T};\theta^\star,\nu^\star)$ for generic controller parameters $(\theta^\star,\nu^\star)$ can be computed analytically. Specifically, $\tilde{J}(y_{1:T},u_{1:T};\theta^\star,\nu^\star)$ is a Gaussian   variable with mean

	\begin{subequations} \label{eqn:GPmeanS}
		\begin{align}
		m_{i}(\theta^\star,\nu^\star) = & \bold{k}_{i}'\left(\bold{K}_{i} + \sigma_e^2I \right)^{-1}\tilde{J}_{1:i},
		\end{align}
		and variance
		\begin{align}
		\sigma_{i}^2(\theta^\star,\nu^\star) = & \kappa((\theta^\star,\nu^\star),(\theta^\star,\nu^\star)) + \\
		- &   \bold{k}_{i}'\left(\bold{K}_{i} + \sigma_e^2I \right)^{-1}\bold{k}_{i} + \sigma_e^2,
		\end{align}
	\end{subequations}
	where the   $j$-th element of the vector $\bold{k}_{i}  \in\mathbb{R}^{i}$ is $\kappa((\theta^\star\!,\!\nu^\star),(\theta_j,\!\nu_j))$;    the $[j,h]$-th entry of the Kernel matrix  $\bold{K}_{i} \in \mathbb{R}^{i \times i}$ is $\kappa((\theta_j,\nu_j),(\theta_h,\nu_h))$;  $\sigma_e^2$ represents the variance of an additive (Gaussian) noise possibly affecting the observations of the cost $\tilde{J}$; and  $I$ denotes the identity matrix of proper dimension.

	The covariance function $\kappa((\theta,\nu),(\tilde \theta,\tilde \nu))$ for the GP can be defined, for instance, in terms of
	the so-called \emph{Squared Exponential} (SE) covariance
	kernel, defined as
	\begin{align*}
	\kappa((\theta,\nu),(\tilde \theta,\tilde \nu))

	= \sigma_0^2 e^{-\frac{1}{2\lambda^2} \left[\theta' - \tilde \theta' \ \mu' - \tilde \mu' \right] \left[\theta' - \tilde \theta' \ \mu' - \tilde \mu' \right]' }.
	\end{align*}
	The hyper-parameters  $\sigma_0$ and $\lambda$ characterizing the SE kernel, as well as the noise variance $\sigma_e^2$, can be chosen by  maximizing the

	log marginal likelihood \cite{williams2006gaussian}

	\begin{align*}
	& \log p(\tilde{J}_{1:i}|\theta_{1:i},\nu_{1:i},\sigma_0,\lambda,\sigma_e) \propto \\
	\propto & -\frac{1}{2}\log \det\left(\bold{K}_{i} + \sigma_e^2I \right)  - \frac{1}{2}\tilde{J}_{1:i}'\left(\bold{K}_{i} + \sigma_e^2I \right)^{-1}\tilde{J}_{1:i}.
	\end{align*}

	%
	\item \emph{Optimization phase} (Steps~\ref{algo:BOv2.2}-\ref{algo:BOv3}). In this phase, the next design parameters $\theta_{i+1}$ and $\nu_{i+1}$ to test are chosen by maximizing the so-called  \emph{acquisition function} $\alpha(\theta,\nu|\mathcal{D})$ (Step~\ref{algo:BOv2.2}). The acquisition function $\alpha(\theta,\nu|\mathcal{D})$ is constructed based on the mean and covariance (eq.~\eqref{eqn:GPmeanS}) of the GP estimated in the learning step. The acquisition function balances  exploration (\emph{i.e.}, learning more about the objective $\tilde{J}$ in regions of the parameter space with high variance) and exploitation (\emph{i.e.}, search over regions with high mean to optimize the expected performance based on past collected data). Different acquisition functions have been proposed in the literature (see \cite{brochu2010tutorial} and the references therein for a deep overview). In the example reported in Section~\ref{Sec:example}, we use the \emph{Expected Improvement} (EI) acquisition function, defined as
	 \begin{align} \label{eqn:EI0}
	\alpha(\theta,\nu|\mathcal{D})=\textbf{EI}(\theta,\nu) = \mathbb{E}\left[\max\{0,\tilde{J}^- \!\!-\! \tilde{J}(\theta,\nu)\} \right],
	\end{align}
	where $\tilde{J}^-$ represents the best value of objective function at the $i$-th iteration, \emph{i.e.},
	\begin{align}
	\tilde{J}^- = \min\limits_{j=1,\ldots,i} \tilde{J}(y_{1:T},u_{1:T};\theta_j,\nu_j).
	\end{align}
	Under the GP framework previously discussed, the EI in~\eqref{eqn:EI0}  can be evaluated analytically and it is equal to:

	\begin{align}  \label{eqn:EI1}
	\textbf{EI}(\theta,\nu)\! =\!\!	\left(\!\tilde{J}^-\! - m_{i}(\theta,\nu)\right)\!\Psi(Z)\! +\!\sigma_{i}(\theta,\nu)\psi(Z)
	\end{align}

	if $\sigma_{i}(\theta,\nu)>0$, $0$ otherwise. In \eqref{eqn:EI1}, $Z$ is defined as
	\begin{align}
	Z = \frac{ \tilde{J}^- - m_{i}(\theta,\nu)  }{\sigma_{i}(\theta, \nu)},
	\end{align}
	and  $\psi$ and $\Psi$ are the \emph{probability density function} and the \emph{cumulative density function} of the standard normal distribution, respectively.
\end{itemize}

The advantages of using BO for tackling this design problem  are twofold. First, it is a derivative-free optimization algorithm, which is useful since a closed-form expression of the performance $\tilde J$ as a function of the design parameters $\theta$ and $\nu$ is not available. Second, it allows us to tune the controller parameters with as few evaluations of $\tilde J$ as possible. The latter point is crucial,  since each evaluation can be costly and time-consuming, as it requires a closed-loop experiment.

\subsection{Restricting the parameter space}

Bayesian optimization allows setting bounds on the  search space of the parameters $\theta$ and $\nu$. These bounds can be included in the maximization of the acquisition function at Step 2.3 of Algorithm 1.
Restricting the search space  generally speeds up the algorithm's convergence, thus requiring fewer evaluations of the functional $\tilde J$.
Suitable bounds may be defined exploiting prior system knowledge and design choices. Some applicable
restrictions of the parameter space are discussed next.

It may be reasonable to assume that the optimal solution is achieved using an inner controller $\mathcal{K}$ that stabilizes the inner loop $\mathcal{M}$.  Therefore, one may constrain $\mu$ so that the prediction model $M$ used by MPC is asymptotically stable.

Some basic control design rules may be also used to restrict the search space of $\theta$ defining the inner-loop controller $\mathcal{K}$.
For instance,  if $\mathcal{K}$ is a PID controller parametrized as in~\eqref{eq:PID}, its static gain should have the same sign of the static gain of the (stable) system $\mathcal{S}$.

Another significant reduction of the parameter space may be achieved under the assumption that the prediction sub-model $M_y(\mu_y)$ used by the MPC accurately describes the system dynamics $\mathcal{M}$ from $g$ to $y$. In this case, one can simply derive the augmented model $M$ providing the relationship from $g$ to the plant input $u$ and output $y$
as
\begin{equation}
\label{eq:aug_model_trick}
\matrice{c}{u \\ y} =  \underbrace{\matrice{c}{K(\theta)(I-M_y(\mu_y)) \\ M_y(\mu_y)}}_{M(\mu_y,\theta)}  g.
\end{equation}
Note that in this case $\mu=\smallmat{\mu_y\\\theta}$, that is the prediction model
and controller share some parameters.

Other restrictions may be introduced according to the particular problem at hand and prior knowledge  available to the user, \emph{e.g.}, diagonal models assuming decoupled
dynamics, grey-box models with known intervals for physical parameters, etc.

\section{Numerical Example}\label{sec:example}
\label{Sec:example}
\begin{figure}[!b]
	\centering
	\includegraphics[width=90pt]{figure/cart_pole.pdf}
	\caption{Schematics of the inverted pendulum on a cart.}
	\label{fig:pendulum_cart}
\end{figure}
As a case study, we consider the control problem of the inverted pendulum on a cart depicted in Fig.~\ref{fig:pendulum_cart}.

\subsection{System description}
The dynamics of the process are governed by the  equations
\begin{subequations} \label{eqn:pend}
	\begin{align}
	(M+m)\ddot p + mL\ddot\phi \cos\phi - mL \dot \phi ^2 \sin \phi + b\dot p &= F, \\
	L \ddot \phi + \ddot p \cos \phi - g \sin\phi + f_\phi\dot \phi &=0 ,
	\end{align}
\end{subequations}
where $p$ is the cart position, $\phi$ is the angle of the pendulum with respect to the upright vertical position, and $F$ is an input force acting on the cart. The following values of the physical parameters are used:  $M=0.5~\text{Kg}$ (cart mass), $m=0.2~\text{Kg}$ (pendulum mass), $L=0.3~\text{m}$ (rod length),
$g=9.81~\text{m/s\textsuperscript{2}}$ (gravitational acceleration), $b=0.1~\text{N/m/s}$, and $f_\phi=0.1~\text{m/s}$ (friction terms). According to the approach proposed in the paper, no knowledge of the physical model of the process is used in  designing the controller, and~\eqref{eqn:pend} are only used for data generation and performance evaluation.

The output signals $p$ and $\phi$
are measured every $T_s=5\;\text{ms}$ and measurements are  corrupted by an additive zero-mean white Gaussian noise with standard deviation $0.01~\text{m}$ and $0.01~\text{rad}$, respectively. The input force $F$ is also  perturbed by an additive zero-mean random disturbance with standard deviation $1~\text{N}$ and bandwidth $10\;\text{rad/s}$.

In performing  closed-loop experiments, the system is initialized at $[p(0) \;  \dot{p}(0)\; \phi(0)\;\dot{\phi}(0)] = [0 \;  0\; \frac{\pi}{20}\;0]$. The   objective is to move the pendulum to the vertical position $\phi=0$, while limiting the cart displacement.
The force $F$ is constrained to belong to the interval  $I_F=[-20\; 20]$~N, while the cart position $p$ should stay within the range $I_p=[-1\   1]$~\text{m} (representing, \emph{e.g.}, finite length of the track where the cart moves).

\subsection{Control design}
The hierarchical controller in Fig.~\ref{fig:hierarchical_outinner} is designed, with $y=[p\ \  \phi]$ and $u=F$.
The inner-loop controller $\mathcal{K}$ is
\begin{equation} \label{eqn:exKin}
u = \left[0\; \; \ \ \mathcal{K}_{PI}(z,\theta) \right](g-y),
\end{equation}
where $\mathcal{K}_{PI}(z,\theta)$ is a discrete-time transfer function of a PID controller parametrized as in \eqref{eq:PID},
with $\theta = [\theta_P\  \theta_I\  \theta_D] \in \mathbb R^3$ and $N_d=100$. Note that only the angle $\phi$ is actually  fed back in the inner loop, thus the task of the inner controller $\mathcal{K}$ is only to stabilize the dynamics of the angle $\phi$.

Besides taking care of  the   control objectives, the outer MPC shall also enforce    constraints on the cart position $p$ and on the input force $F$.
The   model  used by the MPC to predict the dynamics of the inner loop $\mathcal{M}$ from the MPC command $g$ to the plant output  $y=[p \ \ \phi]$  (see Fig.~\ref{fig:hierarchical_outinner}) is parameterized as the continuous-time state-space model
\begin{equation} \label{eqn:exMM}
\dot \xi_M = A_{M}(\mu) \xi_M + B_{M}(\mu) g, \ \ \ y_M    = \xi_M,
\end{equation}
where $\xi_M\in\mathbb{R}^2$ is the state vector and $\mu \in \mathbb R^{6}$ contains the  entries of   $A_M \in \mathbb R ^{2\times 2}$ and the second column of $B_M \in \mathbb R ^{2 \times 2}$. Because of the structure of the inner controller $\mathcal{K}$ in~\eqref{eqn:exKin}, the position $p$ is not fed back to the inner loop. Thus,  the first column of $B_M$ is set to zero and   not included in the design  parameter vector $\mu$.
The overall MPC prediction model $M$
is constructed using~\eqref{eq:aug_model_trick}.

The MPC control law is computed solving~\eqref{eq:MPC} and applied in a receding-horizon fashion, using a sampled version of~\eqref{eqn:exMM}
with sampling time  $T_{\text{MPC}} = 10 T_s = 50\;\text{ms}$,
reference $r = [r_p \  r_\phi] = [0 \ \  0]$ and real-time constraints on $F$ and $p$ based on the  admissible intervals $I_F$ and $I_p$, respectively.

Regarding the MPC design parameters, the weight matrices are not optimized and set  to
$Q_y=\text{diag}(0.1,0.1)$, $Q_u = 0$,  $Q_{\Delta u} = 0.1$ and $Q_\epsilon = 10^5$. The prediction horizon $N_p$ is considered as a free parameter to be adjusted in the Bayesian optimization, while $N_u$ is set equal to $N_p$. The real-value design parameters $\theta$ and $\mu$ are constrained to belong to the interval $[-500 \ \ 500]$, while the prediction horizon $N_p$ can take integer values between $10$ and $20$.
The MPC control law is computed using the MATLAB \emph{Model Predictive Control Toolbox}. All the computations are carried out on an i5 2.60-GHz Intel  core  processor  with  32  GB  of  RAM  running  MATLAB R2018a.
The maximum computational time required to evaluate the MPC law over all the performed closed-loop experiments is  $21$ ms, thus lower than the  sampling time $T_{\text{MPC}}=50$ ms.

Overall, there are $10$   parameters to be designed, namely $\theta \in \mathbb{R}^3$, $\mu \in  \mathbb{R}^6$, and $N_p \in  \mathbb{N}$. The  closed-loop performance cost $\tilde{J}$ to be minimized is defined as
\begin{multline} \label{eqn:JCex}
\tilde{J} = \log \bigg[\frac{1}{T} \sum_{t=1}^T \bigg (\frac{1}{10}|r_p - p(t)|  + \frac{9}{10}|r_\phi - \phi(t)| \bigg ) \bigg ] +\\
+ \log \bigg[ \frac{1}{T}\sum_{t=1}^T b(p(t)) \;\;+1\bigg],
\end{multline}
where
\begin{equation}
b(p) = \begin{cases}
10(|p|-1) \;\;\;  \;\;\;\text{for } |p| > 1,\\
0 \qquad \qquad \;\;\;\;\;\   \text{for } |p| \leq  1.
\end{cases}
\end{equation}
is the barrier function taking into account violation on the physical constraints on the cart position $p$.
The   cost $\tilde{J}$   is evaluated over closed-loop  experiments of length $10$~s on the discrete-time samples collected at rate $T_s$. This objective function reflects the engineering objective of controlling the angle $\phi$ to 0, limiting the horizontal displacement and keeping the cart position in the admissible range $I_p$. The constraint on the force $F$ is enforced by a saturation block at the system input, and thus it is not penalized  in $\tilde{J}$.

The design problem \eqref{eqn:costCLtot} is solved using the
MATLAB \emph{Statistics and Machine Learning Toolbox}, setting the \textbf{EI} in \eqref{eqn:EI0} as acquisition function. $N_{\text{in}} = 10$ random  values of the design parameters $\theta$, $\mu$ and $N_p$ are generated  to initialize Algorithm \ref{algo:BO}, which is then executed for $310$ iterations.
The complete test code of this paper is available for download at \url{http://www.marcoforgione.it/data/code/CSL2019_perf.zip}.

\subsection{Simulation results}

The performance cost $\tilde J$ \textit{vs} the iteration index $i$ of Algorithm~\ref{algo:BO} is shown in Fig.~\ref{fig:iteration}. For each iteration $i$, the performance of the current test point (black asterisk) and of the current best point up to iteration $i$ (red line) are shown.
From Fig.~\ref{fig:iteration}, it can be noticed that the  optimal controller parameters are found at iteration $123$ (green square). Furthermore,  as the iteration index $i$ increases, more and more test points are concentrated
in an area of low cost $\tilde J$.

A closed-loop experiment is repeated over a longer period of $20$~s using the designed controller. The time trajectories of the cart's position $p$ and the pendulum's
angle $\phi$ are plotted in Fig.~\ref{fig:position_angle}, which shows that the designed controller is able to stabilize the pendulum' angle in the upright vertical position, respecting the constraints on the cart's position $p$.

\begin{figure}[!b]
	\centering
	\includegraphics[width=174pt]{figure/iterations.pdf}
	\caption{Performance cost $\tilde J$ \emph{vs} iteration   $i$ of Algorithm~\ref{algo:BO}.}
	\label{fig:iteration}
\end{figure}

\begin{figure}[!b]
	\centering
	\includegraphics[width=174pt]{figure/position_angle_vs_t.pdf}
 	\caption{Closed-loop experiment: position $p$ of the cart with the considered bounds (top plot) and pendulum's angle $\phi$ (bottom plot).}
	\label{fig:position_angle}
\end{figure}

For the sake of comparison, the following two non-hierarchical  model-based controllers are designed  based on the physical model of the system (Eq.~\eqref{eqn:pend}) linearized around   $[p(0) \;  \dot{p}(0)\; \phi(0)\;\dot{\phi}(0)] = [0 \;  0\; 0 \;0]$:
\begin{itemize}
	\item an MPC,  with the same sampling rate $T_{\text{MPC}}=50$~ms considered before, which reflects   real-time constraints.  At this sampling rate, the MPC is not able to reject the disturbance and thus fails to stabilize the pendulum around the upright vertical position.  This  shows the advantages of the  hierarchical multi-rate controller structure.

	\item a \emph{Linear-Quadratic-Gaussian} (LQG) controller, with sampling rate $T_s=5$~ms. This controller stabilizes the pendulum. However, besides requiring a knowledge of the plant,  it achieves a performance cost $\tilde{J}$ (eq. \eqref{eqn:JCex}) equal to $-2.41$, which is worse than   the cost $\tilde{J}=-3.66$ obtained using  the proposed performance-oriented approach.

\end{itemize}

\section{Conclusions and follow-up}

In this work, we described a method to learn \textit{MPC-oriented models} for hierarchical control schemes via iterative closed-loop experiments. We showed that such experiments can be suitably designed using Bayesian optimization. In the proposed learning framework, the model does not necessarily provide the highest input/output data fit, which is the typical objective of system identification, but is the one yielding the model-based controller corresponding to the best closed-loop performance. We also argued that the prediction horizon can be optimized using the same tools and experiments. Numerical simulations on a benchmark example showed that data can lead to satisfactory controllers with no knowledge of the system dynamics and no constraints on modeling accuracy. Future research will be devoted to the theoretical analysis of the proposed learning strategy as well as to its experimental validation on a real-world setup.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


What are the most important rooms for an investment property?
Are you looking to make more of your residential property investment? While property portfolio diversification is an excellent option, so is ensuring that your current properties are up to scratch. But where should you start? Where do you have room for improvement?
Renovations can be as big or as small as you want.
The kitchen is central to a good family home – it isn't just a place for cooking, it's a focal point of the family social life, and, if you're going for pure value-added renovations, you could do a lot worse than upgrading your kitchen.
Quantity surveyor and general manager of Construction Costs Consultants Gary Caulfield described to Westpac how renovations of kitchens can pay you back $1.50 for every dollar spent on the spruce up, making it second only to constructing additional bedrooms.
The number of bedrooms in a house is one of the defining valuation features, but just how important is it for the wellbeing of your tenants? Any good landlord will want to ensure that their tenants are happy and healthy in their property, and a good bedroom could be integral to that.
A survey commissioned by Sleepyhead showed that almost half of New Zealanders were woken during the night by discomfort and pain, while 13 per cent wake up every night and have difficulty getting back to sleep. You might be surprised how much of a difference a decent bedroom (and a comfortable, orthopaedic mattress) could make.
There are plenty of ways to improve your residential investment property.
A recent global report from Nielsen revealed that the most common recreational activity in the world is watching television. Every single generation from the silent generation to millennials selected watching TV to be their favourite spare-time activity. With the exception of Generation Z (15 to 20 years old), television outpaced music, reading and even travel.
This research highlights the importance of the living room, as it is bound to be one of the most common places to watch television. Ensure that this room is up to scratch, and give your tenants or buyers a great room to experience their favourite programs.
There are plenty of ways to improve your residential investment property, but these are a good place to start. Every room is important in some way, so make sure your entire portfolio is up to scratch and ready to build you some wealth.


Best scripts for interior real estate video?
Since I have done nothing to it yet, my maximum resolution is 2304x1296 30p. For maximum sharpness should I be using the script for 2560x1440 30p? What bitrate should I use?
When filming a bright window, auto exposure causes the rest of the room to become darker. What script do I use to turn auto exposure off, and how does it work? Do I need to put the exposure in the script, so I won't be able to change exposure later in the field?
Same question for auto white balance. Different room lights cause the white balance to change and I get the white flash. Can I lock the WB? Do I change the WB when I make the script, or am I able to change it as needed in the field?
Are there any de-noising settings that would help me?
Thanks for any help, sorry if my questions are vauge, I'm new to this camera and the process of scripts.
For that kind of filming I would use normal camera instead of plastic toy.


text:
Main Blog > 12,000-year-old Skeleton From Underwater Mexican Cave
12,000-year-old Skeleton From Underwater Mexican Cave
Miss Cellania • Tuesday, May 20, 2014 at 1:00 PM
Scientists have confronted a mystery of how the Americas were first settled. The first people who walked across the Bering Strait from Asia around 20,000 years ago are the ancestors of Native Americans, yet modern people look rather different from those first immigrants. A theory that explains this difference is that other migrations took place afterward, and the different waves of immigrants interbred. DNA evidence from a skeleton found in an underwater cave sheds some light on the question.
Between 12,000 and 13,000 years ago, a 15-year-old girl wandered into a cave now called Hoyo Negro ("black hole") on the Yucatan Peninsula in Mexico and died. Then the Ice Age glaciers melted and the cave was filled with water. In 2007, divers discovered her skeleton and the DNA of the girl they call Naia has since been sequenced.
Tests on mitochondrial DNA taken from Naia show that she had a genetic marker common today across the Americas, one that scientists say evolved in a prehistoric population that had been isolated for thousands of years in Beringia, the land mass between Alaska and Siberia that formed a bridge between the continents during the Ice Ages.
Thus, according to the report, the Native Americans and the Paleoamericans are the same people, descended from the same Beringia population. They just look different because of recent evolution.
Read more about the discovery of the extremely old teenager at The Washington Post. -via TYWKIWDBI
(Image credit: Science magazine, via The Washington Post)
Tags: Paleoamerican, Ancient Skeleton, Yucatan, Native American, DNA, Paleontology
Did The Oldest Settlers in North America Live in Texas?
Did A Comet Wipe Out The Mammoths?
Fresco from 1494 Has the First Known Depiction of Native Americans in European Art
Did Prehistoric Native Americans Recorded a Supernova?
Buffalo Jump: Native American's Way to Kill Herds of Buffalos.
American Gothic Socks
"12,000-year-old Skeleton From Underwater Mexican Cave"

redpajama_set_name: RedPajamaCommonCrawl


A food-focus versus nutrient-focus to nutrition means that if we eat real food, our bodies will naturally receive the nutrients they need. In other words, this approach means that we can nourish our bodies while enjoying our food without the need for the latest diet fads, supplements or shakes. The key is to eat a wide variety of foods from the five food groups each day.
What do the food groups provide?
We know that foods from each food group contain similar nutrients that sustain our bodies. This makes healthy eating flexible as foods within each food group can be substituted to meet individual dietary requirements such as Coeliac disease, allergies, intolerances and lifestyle preferences such as vegetarianism or veganism. The body of evidence also tells us that consuming foods from the five food groups can support optimal health and prevent disease. The benefits of each of the five food groups is listed below.
Key nutrients – carbohydrates, dietary fibre, protein, iron, thiamin, folate, iodine, magnesium, zinc, riboflavin, niacin and vitamin E.
Key nutrients – dietary fibre, carotenoids including beta-carotene, vitamin C, folate, carbohydrates, magnesium, iron and potassium.
There is, however, a suggested link between increased risk of some cancers and preserved vegetable consumption (salted, dried, fermented, pickled). As always, everything in moderation.
Key nutrients – vitamin C, dietary fibre, carbohydrates, folate, beta-carotene and potassium.
Key nutrients – calcium, protein, riboflavin, vitamin B12, fats, carbohydrates, magnesium, zinc and potassium.
Key nutrients – protein, fats including omega-3 fatty acids, iron, zinc, vitamin B12, niacin and vitamin E.
There is, however, a likely link between increased risk of some cancers with daily red meat consumption (100-120g/day).
The Australian Guide to Healthy Eating shows us in picture-form the amounts of each food group recommended to be eaten each day. For more detail, the healthy eating brochures for adults and for children illustrate what a serve of each food group is and the number of serves recommended to meet healthy individuals' needs.
Interview with Tim Crowe: what would Tim do?


** NOTE: A newer report on this subject appears here. **Incheon airfield in Seoul, south-central Korea is one of the largest and best airports anyplace in the world. If you fly in Asia, there is a good enough hazard you'll stop in that respect at slightest once.
The Indochina drag is southeast eastward Asia's all but infamous travel route. A fashionable path crosses 3 countries, namely Thailand, Cambodia & Vietnam. It frequently extends to Malaysia, Laos, Myanmar, and Singapore.
Hong Kong (香港 Heūng Góng in Cantonese, signification Fragrant Harbor) is a Special body Region (SAR) of the People's form of government of China. It is a place with multiple personalities as a result of being some cantonese formosan and having been below country colonization. Today, the other British body is a stellar tourism name and address for China's more and more affluent mainland population.


Joseph Turner is a native of Bowdon, Georgia, and a graduate of the University of West Georgia. He was a member of the West Georgia football team that made the playoffs in 1996 and won the Gulf South Conference in 1997. He received the Iron Man Award for Spring Practice at West Georgia in 1997.
He enters his 17th year of teaching and currently teaches at Manchester High School. Before coaching at Manchester, he was a Head Middle School Coach, Head Junior Varsity Coach, Varsity Assistant, and Varsity Defensive Coordinator. Turner is also a Certified Strength and Conditioning Specialist with the National Strength and Conditioning Association, and a Level I Sports Performance Coach through USA-Weightlifting.


Rights This resource is provided for educational purposes only. Media is owned by the Peoria Historical Society or other persons/entities; images may not be downloaded, reproduced, or distributed in any format without written permission. Any attempt to download, copy in any manner, and/or circumvent the access controls placed on these files is a violation of United States and international copyright laws and is subject to criminal prosecution. For more information, contact the Special Collections Center of the Bradley University Library (309) 677-2822 or frey@bradley.edu.


text:
\section{Approach}

\subsection*{Bootstrapping with representation learning}
\label{sec:embed}

Our algorithm iteratively grows a pool of multi-word entities ($\textrm{entPool}_c$) and $n$-gram patterns ($\textrm{patPool}_c$) for each category of interest $c$, and learns custom embeddings for both, which we will show are crucial for both performance and interpretability.

The entity pools are initialized with a few seed examples ($\textrm{seeds}_c$) for each category.
For example, in our experiments we initialize the pool for a {\small {\tt person names}} category with 10 names such as {\em Mother Teresa}.
Then the algorithm iteratively applies the following three steps for $T$ epochs:
{\flushleft {\bf (1)} {\em Learning custom embeddings}}: The algorithm learns custom embeddings for all entities and patterns in the dataset, using the current {$\textrm{entPool}_c$}s as supervision. This is a key contribution, and is detailed in the second part of this section.
{\flushleft {\bf (2)} {\em Pattern promotion}}: We generate the patterns that match the entities in each pool $\textrm{entPool}_c$, rank those patterns using point-wise mutual information (PMI) with the corresponding category, and select the top ranked patterns for promotion to the corresponding pattern pool $\textrm{patPool}_c$. {\flushleft {\bf (3)} {\em Entity promotion}}: Entities are promoted to $\textrm{entPool}_c$\ using a multi-class classifier that estimates the likelihood of an entity belonging to each class~\cite{gupta2015distributed}.
Our feature set includes, for each category $c$: (a) edit distance between the candidate entity $e$ and current $e_c$s $\in$ $\textrm{entPool}_c$, (b)
the PMI (with $c$) of the patterns in $\textrm{patPool}_c$\ that matched $e$ in the training documents,
and (c) similarity between $e$ and $e_c$s in a semantic space.
For the latter feature group, we use two sets of vector representations for entities.
The first is the set of embedding vectors learned in step (1). The second includes pre-trained word embeddings; for multi-word entities and patterns, we simply average the embeddings of the component words.
We use these vectors to compute the cosine similarity score of a given candidate entity $e$ to the entities in $\textrm{entPool}_c$, and add the average and maximum similarities as features.
The top 10 entities classified with the highest confidence for each class are promoted to the corresponding $\textrm{entPool}_c$\ after each epoch.

\subsection*{Learning custom embeddings}

We train our embeddings for both entities and patterns by maximizing the objective function $J$:
\begin{equation}
\small
\label{eq:J}
  J = \text{SG} + \text{Attract} + \text{Repel}
\end{equation}
where SG, Attract, and Repel are individual components of the objective function designed to model both the unsupervised, language model part of the task as well as the light supervision coming from the seed examples, as detailed below.

The SG term captures the original objective function of the {\em S}kip-{\em G}ram model of ~\citet{mikolov2013distributed}, but, crucially, adapted to operate over multi-word entities and contexts consisting not of bags of context words, but of the patterns that match each entity:
\begin{equation}
\small
  \label{eq:rank}
    \begin{aligned}
	\text{SG} = \sum_{e} [ \log(\sigma(V_e^\top V_{pp})) + \sum_{np} \log(\sigma(-V_e^\top V_{np})) ]
    \end{aligned}
\end{equation}
where $e$ represents an entity, $pp$ represents a positive pattern, i.e., a pattern that matches entity $e$ in the training texts, $np$
represents a negative pattern, i.e., it has not been seen with this entity,
and $\sigma$ is the sigmoid function.
Intuitively, this component forces the embeddings of entities to be similar to the embeddings of the patterns that match them, and dissimilar to the negative patterns.

The second component, Attract, encourages entities or patterns in the same pool to be close to each other. For
example, if we have two entities in the pool known to be person names, they
should be close to each other in the embedding space:
\begin{equation}
\small
  \text{Attract} = \sum_{P} \sum_{x1,x2 \in P} \log(\sigma(V_{x1}^\top V_{x2}))
\end{equation}
where $P$ is the entity/pattern pool for a category, and $x1,x2$ are entities/patterns in said pool.

Lastly, the third term, Repel, encourages that the pools be mutually exclusive, which is a soft version of the counter training approach of~\citet{yangarber03} or the weighted mutual-exclusive bootstrapping algorithm of~\citet{mcintosh2008weighted}. For example,
person names should be far from organization names in the semantic embedding space:
\begin{equation}
\small
  \text{Repel} = \sum_{P1,P2 \text{ if } P1 \ne P2} \sum_{x1 \in P1} \sum_{x2 \in P2} \log(\sigma(-V_{x1}^\top V_{x2}))
\end{equation}
where $P1,P2$ are different pools, and $x1$ and $x2$ are entities/patterns in $P1$, and $P2$, respectively.

We term the complete algorithm that learns and uses custom embeddings as {\em Emboot} ({\em Em}beddings for {\em boot}strapping) and the stripped-down version without them as {\em EPB} ({\em E}xplicit {\em P}attern-based {\em B}ootstrapping).\footnote{EPB is similar to \cite{gupta2015distributed}; the main difference is that we use pretrained embeddings in the entity promotion classifier rather than Brown clusters.}

\subsection*{Interpretable model}

\label{sec:interp}

In addition of its output ($\textrm{entPool}_c${s}), Emboot produces custom entity and pattern embeddings that can be used to construct a a decision-list model, which provides a global, deterministic interpretation of what Emboot learned.

This interpretable model is constructed as follows.
First, we produce an average embedding per category by averaging the embeddings of the entities in each $\textrm{entPool}_c$.
Second, we estimate the cosine similarity between each of the pattern embeddings and these category embeddings, and convert them to a probability distribution using a softmax function; $prob_c(p)$ is the resulting probability of pattern $p$ for class $c$.
Third, each candidate entity to be classified, $e$, receives a score for a given class $c$ from all patterns in $\textrm{patPool}_c$\ that match it.
The entity score aggregates the relevant pattern probabilities using Noisy-Or:

\begin{scriptsize}
\begin{equation}
\small
    Score(e, c) = 1 - \prod_{\{ p_c \in patPool_c \mid matches(p_c, e) \}} (1 - prob_c(p_c))
\end{equation}
\end{scriptsize}Each entity is then assigned to the category with the highest overall score.

\section{Related Work}

Bootstrapping is an iterative process that alternates between learning representative patterns, and acquiring new entities (or relations) belonging to a given category~\cite{riloff1996automatically,mcintosh2010unsupervised}.
Patterns and extractions are ranked using either formulas that measure their frequency
and association with a category,
or classifiers, which increases robustness due to regularization~\cite{carlson2010coupled,gupta2015distributed}.

Distributed representations of words~\cite{mikolov2013distributed,levy2014dependency} serve as underlying representation for many NLP tasks such as information extraction and question answering \cite{riedel2013relation,toutanova2015representing,toutanova2016compositional,Sharp2016}.
However, most of these works that customize embeddings for a specific task rely on some form of supervision.
In contrast, our approach is lightly supervised, with a only few seed examples per category.
\citet{batista2015emnlp}  perform bootstrapping for relation extraction using pre-trained word embeddings.
They do not learn custom pattern embeddings that apply to multi-word entities and patterns. We show that customizing embeddings for the learned patterns is important for interpretability.

Recent work has focused on explanations of machine learning models that are model-agnostic but local, i.e., they interpret individual model predictions \cite{anchors:aaai18,lime:kdd16}.
In contrast, our work produces a global interpretation, which explains the entire extraction model rather than individual decisions.

Lastly, our work addresses the interpretability aspect of information extraction methods.
Interpretable models mitigate
the technical debt of machine learning~\cite{43146}. For example, it allows
domain experts to make manual, gradual improvements to the models. This is why
rule-based approaches are commonly used in industry applications, where software maintenance is
crucial~\cite{chiticariu2013rule}.
Furthermore, the need for interpretability also arises in critical systems, e.g., recommending treatment to patients, where these systems are deployed to aid human decision makers~\cite{rudin17}.
The benefits of interpretability have
encouraged efforts to either extract interpretable models from opaque
ones~\cite{craven1996extracting}, or to explain their
decisions~\cite{ribeiro2016should}.

As machine learning models are becoming more
complex, the focus on interpretability has become more important, with new
funding programs focused on this topic.\footnote{
DARPA's
  Explainable
  AI program: {\scriptsize
    \url{http://www.darpa.mil/program/explainable-artificial-intelligence}}.}
    Our approach for exporting an interpretable model (\S\ref{sec:interp}) is similar to \citet{valenzuela2016snaptogrid}, but we start from distributed representations, whereas they started from a logistic regression model with explicit features.

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

\begin{figure*}[t!]
  \centering
  \vspace*{-4mm}
  \subcaptionbox{Embeddings initialized randomly\label{fig:bs_epoch_0}}{\includegraphics[width=0.3\textwidth]{figs/sgd_final_words_epoch_0.pdf}}\hspace{0em}%
  \subcaptionbox{Bootstrapping epoch 5\label{fig:bs_epoch_5}}{\includegraphics[width=0.3\textwidth]{figs/sgd_final_words_epoch_5.pdf}}\hspace{0em}%
  \subcaptionbox{Bootstrapping epoch 10\label{fig:bs_epoch_10}}{\includegraphics[width=0.3\textwidth]{figs/sgd_final_words_epoch_10.pdf}}\hspace{0em}%
  \vspace{-2mm}
    \caption{t-SNE visualizations of the entity embeddings at three stages during training. \\ Legend: \tikz\draw[blue,fill=blue] (0,0) circle (.5ex); = \texttt{LOC}. \tikz \fill [black!60!green](0.1,0.1) rectangle (0.3,0.3); = \texttt{ORG}. \tikz{\filldraw[draw=magenta,fill=magenta] (0,0) --
(0.2cm,0) -- (0.1cm,0.2cm);} = \texttt{PER}. \tikz{\filldraw[draw=red,fill=red, rotate=180] (0,0) --
(0.2cm,0) -- (0.1cm,0.2cm);} = \texttt{MISC}.}
  \label{fig:embeddings}
  \vspace{-4mm}
\end{figure*}

\begin{figure*}[th!]
\centering
\begin{tabular}{ c }

\includegraphics[width=0.8\textwidth]{results/conll/plot1/plot-overall.pdf}
\\
\includegraphics[width=0.8\textwidth]{results/ontonotes/plot1/plot-overall.pdf}

\end{tabular}
\vspace*{-2mm}
\caption{\small Overall results on the  CoNLL and Ontonotes datasets. Throughput is the number of entities classified, and precision is the proportion of entities that were classified correctly.
Please see Sec.~\ref{sec:experiments} for a description of the systems listed in the legend.
}
\label{fig:results}
\vspace{-2mm}
\end{figure*}

We evaluate the above algorithms on the task of named entity classification from free text.

{\flushleft {\bf Datasets}:}
We used two datasets, the CoNLL-2003 shared task dataset~\cite{conll_dataset}, which contains 4 entity types, and the Ontonotes dataset~\cite{ontonotes}, which contains 11.\footnote{We excluded numerical categories such as \texttt{DATE}.}
These datasets contain  marked entity boundaries
with labels for each marked entity. Here we only use the entity boundaries but {\em not} the labels of these entities during the training of our bootstrapping systems.
To simulate learning from large texts, we tuned hyper parameters on development, but ran the actual experiments on the {\em train} partitions.

{\flushleft {\bf Baselines}:} In addition to the EPB algorithm, we compare against the approach proposed by~\citet{gupta2014improved}\footnote{\tiny{\url{https://nlp.stanford.edu/software/patternslearning.shtml}}}.
This algorithm is a simpler version of the EPB system, where entities are promoted with a PMI-based formula rather than an entity classifier.\footnote{We did not run this system on Ontonotes dataset as it uses a builtin NE classifier with a predefined set of labels which did not match the Ontonotes labels.}
Further, we compare against label propagation (LP)~\cite{zhu02learning}, with the implementation available in the {\tt scikit-learn} package.\footnote{\tiny{\url{http://scikit-learn.org/stable/modules/generated/sklearn.semi_supervised.LabelPropagation.html}}}
In each bootstrapping epoch, we run LP, select the entities with the lowest entropy, and add them to their top category. Each entity is represented by a feature vector that contains the co-occurrence counts of the entity and each of the patterns that matches it in text.\footnote{We experimented with other feature values, e.g., pattern PMI scores, but all performed worse than raw counts.}

{\flushleft {\bf Settings}:} For all baselines and proposed models, we used the same set of 10 seeds/category, which were manually chosen from the most frequent entities in the dataset. We used dependency-based word embeddings~\citep{depEmbed} of size 300 as the predefined embedding vectors in the entity promotion classifier.
For the custom embedding features, we used randomly initialized 15d embeddings.
Here we consider patterns to be $n$-grams of size up to 4 tokens on either side of an entity. For instance, ``\texttt{@ENTITY , former President}'' is one of the patterns learned for the class {\tt person}.
We ran all algorithms for 20 bootstrapping epochs, and the embedding learning component for 100 epochs in each bootstrapping epoch.
We add 10 entities and 10 patterns to each category during every bootstrapping epoch.

\section{Discussion and Conclusion}

Before we discuss overall results, we provide a qualitative analysis of the learning process for Emboot for the CoNLL dataset in Figure~\ref{fig:embeddings}.
The figure shows t-SNE visualizations~\cite{van2008visualizing}
of the entity embeddings at several stages of the algorithm. This visualization matches our intuition: as training advances, entities
belonging to the same category are indeed grouped together.
In particular, Figure~\ref{fig:bs_epoch_10} shows five clusters, four of which are dominated by one category (and centered around the corresponding seeds), and one, in the upper left corner, with the entities that haven't yet been added to any of the pools.

A quantitative comparison of the different models on the two datasets is shown in Figure~\ref{fig:results}.

Figure~\ref{fig:results} shows that Emboot considerably outperforms  LP and \citet{gupta2014improved}, and has a small but consistent improvement over EPB. This demonstrates the value of our approach, and the importance of custom embeddings.

Importantly, we compare Emboot against: (a) its interpretable version (Emboot$_{int}$), which is constructed as a decision list containing the patterns learned (and scored) after each bootstrapping epoch, and (b) an interpretable system built similarly for EPB (EPB$_{int}$), using the pretrained Levy and Goldberg embeddings rather than our custom ones.
This analysis shows that Emboot$_{int}$ performs close to Emboot on both datasets, demonstrating that most of the benefits of representation learning are available in an interpretable model.
Further, the large gap between Emboot$_{int}$ and EPB$_{int}$ indicates that the custom embeddings are critical for the interpretable model.

Note that Emboot$_{int}$'s decisions are easy to interpret. Due to the sparsity of patterns, the majority of predictions are triggered by 1 or 2 patterns. For example, the entity ``Syrian'' is correctly classified as MISC (which includes  demonyms) due to two patterns matching it in the CoNLL dataset: ``{\tt @ENTITY President}'' and ``{\tt @ENTITY troops}''. In general, for the CoNLL dataset, 59\% of Emboot$_{int}$'s predictions are triggered by 1 or 2 patterns; 84\% are generated by 5 or fewer patterns; only 1.1\% of predictions are generated by 10 or more patterns.

This work demonstrates that representation learning can be successfully combined with traditional, pattern-based bootstrapping, yielding models that perform well despite the limited supervision, and that are interpretable, i.e., end users can understand why an extraction was generated.

\section{Introduction}

One strategy for mitigating the cost of supervised learning in information extraction (IE) is to bootstrap extractors
with light supervision from a few provided examples (or seeds).
Traditionally, bootstrapping approaches iterate between learning extraction patterns such as word $n$-grams, e.g., the pattern ``{\tt @ENTITY , former president}'' could be used to extract person names,\footnote{In this work we use surface patterns, but the proposed algorithm is agnostic to the types of patterns learned.} and applying these patterns to extract the desired structures (entities, relations, etc.)~\cite[inter alia]{carlson2010coupled,gupta2014improved,gupta2015distributed}. One advantage
of this direction is that these patterns are interpretable,
which mitigates the maintenance cost associated with machine learning systems~\cite{43146}.

On the other hand, representation learning has proven to be useful for natural
language processing (NLP) applications~\cite[inter alia]{mikolov2013distributed,riedel2013relation,toutanova2015representing,toutanova2016compositional}.
Representation learning approaches often include a component that is trained in an unsupervised manner, e.g., predicting words based on their context from large amounts of data,
mitigating the brittle statistics
affecting traditional bootstrapping approaches. However, the resulting real-valued embedding vectors are hard to interpret.

Here we argue that these two directions are complementary, and should be combined.
We propose such a bootstrapping approach for information extraction (IE), which blends the advantages of both directions. As a use case, we instantiate our idea for named entity classification (NEC), i.e., classifying a given set of unknown entities
 into a predefined set of categories~\cite{collins99}.
The contributions of this work are:

{\flushleft {\bf (1)}} We propose an approach for bootstrapping NEC that iteratively  learns custom embeddings for {\em both} the multi-word entities to be extracted and the patterns that match them from a few example entities per category.
Our approach changes the objective function of a neural network language models (NNLM) to include a semi-supervised component that models the known examples, i.e., by {\em attracting} entities and patterns in the same category to each other and {\em repelling} them from elements in different categories, and it adds an external iterative process that ``cautiously'' augments the pools of known examples~\cite{collins99}.

{\flushleft {\bf (2)}}  We demonstrate that our representation learning approach is
suitable for semi-supervised NEC. We compare our approach against several state-of-the-art semi-supervised approaches on two datasets: CoNLL-2003~\cite{conll_dataset} and OntoNotes~\cite{ontonotes}.
We show that, despite its simplicity, our method outperforms all other approaches.

{\flushleft {\bf (3)}}
Our approach also outputs an interpretation of the learned model, consisting of a decision list of patterns, where each pattern gets a score per class based on the proximity of its embedding to the average entity embedding in the given class. This interpretation is global, i.e., it explains the entire model rather than local predictions.
We show that this decision-list model performs comparably to the complete model on the two datasets. This guarantees that the resulting system can be understood, debugged, and maintained by non-machine learning experts.
 Further, this model outperforms considerably an interpretable model that uses pretrained embeddings, demonstrating that our custom embeddings help interpretability.


Located in the heart of Benidorm and only 850m from the famous Levante beach, this hotel provides guests with comfortable accommodation and a host of amenities. Perfect for both families and couples alike looking for a lively Benidorm holiday.
The hotel is 850m from Playa de Levante.
Alicante Airport is 60 km from the hotel.
Hotel Joya provides guests with a choice of Single, Double, Triple or family-style rooms. All accommodation features air conditioning, a private bathroom with hairdryer and a balcony or terrace. Other features include a TV, telephone, desk and wake-up service.
The hotel's restaurant serves international cuisine in a buffet style and is open for breakfast, lunch and dinner. There is also an on-site bar with a varied drinks menu.
Hotel Joya has a large outdoor swimming pool with separated children's section, water features and a Jacuzzi-style area. The animation team host a range of daily activities for all ages and a dedicated kids club through July and August. There is also a games room with pool table and internet corner.


I have been a part of the telemarketing world for many years and work with a wide array of different clients, but I always get excited when I find out that Quality Contact Solutions landed a new client that needs help with membership retention outbound telemarketing calls. Associations provide many valuable benefits as part of their value proposition to their members. From certification programs to meetings and conventions, to networking, associations are ideally positioned to meet the unique needs of their specific members.
First and foremost, front line call center agents like to put a face with the name of the client. Who are they making these calls for and why is the membership important to the person that we will be calling? Having the clients be present, providing their knowledge and encouragement, can make a world of difference in how our front-line team performs.
We have all heard the phrase "smile and dial". I don't know how many times I say this during trainings and monitoring sessions. You can "hear" a smile! Starting the call off with a pleasant tone will help the agent connect faster with the member. Have the agents' role play before shift to make sure that you can "hear" the smile. If an agent is happy and energetic, it will also help their tone and pace. Many members that we call are busy professionals. They are thankful for our call, but they need to get the renewal done quickly without feeling like they were slighted in customer service.
This one goes back to the initial training and making sure that the front-line agent has all the tools needed. The script (also known as a call guide) should be clear and concise. This includes immediately stating right away who the agent is calling on behalf of, what type of membership is up for renewal and the price to renew. The agent should also have access to Member's ID and expiration dates so that they can speak to the member intelligently. The agent needs to also be well versed in the processes, especially if payment is being taken. Having the knowledge to walk the member though each piece of the renewal is key.
A telemarketing agent is not going to be an expert on each association right away, but you need to provide them with FAQs and common Q&A's, so they can feel comfortable contacting the member. No one wants to feel like they are ill-equipped for placing a call. They need to know the basic information and they also need the proper contact information for times that they cannot answer a question or if a member has a concern.
Preparing the agent to listen carefully to the member is very important. A lot of times, members will offer pertinent information that the association would like to have notated. It may be why they have not renewed or why they love the membership so much. Feedback and transparency helps ensure a fantastic long-term relationship and is the key for future success.
This entry was posted in Articles and tagged Associations, B to B Telemarketing, B2B Outbound Marketing, B2B Telemarketing, Telemarketing Services on January 23, 2018 by .


A celebrative event for our members!
Remember 2005 Cabernet Sauvignon, 2006 Nonna Viola, 2007 Saint Helena Syrah, Ottimista Chenin Blanc?
We have properly cellared these gorgeous, decorated wines and have up to 3 cases (some vintages less) available for purchase. First come, first serve. Call (509) 591.2853 or email kim@anelare.com to reserve!
Happy Fall, and see you on Oct. 30th!


Well, it has been an interesting week here - we've had snow days, work days, last night was quilt guild and today was Bunco! That didn't leave a lot of time for sewing, but that's ok - I did manage to get my new Island Batik fabrics all snuggled into their spot in my sewing room and am ready to start working on the Mavelous Mini challenge. I even have a pattern picked out and fabric pulled, so I just need to start cutting!
Are you ready for that giveaway I promised?
I have some leftover 10 inch squares (27 in all!), a couple of stash builder rolls, a hunk of Buttercream foundations and an Island Batik Pin! This will make a nice little bundle for someone to play with.
I thought it would be fun to have this giveaway go through Valentine's Day and I'll do the drawing the day after. I really appreciate all of you for reading along and encouraging me in my quilting adventures (and all the other odds and ends that go on around here!). It wouldn't be near as much fun to write my blog if no one were reading it!
Oh, and in other good news, I'll also start this years Block of the Month next Friday! I had a bit of a delay due to the changes at Craftsy, and dealing with that set me back a bit, but now I am ready to get started. Seashore Splendor will feature a nautical theme, with blocks in different sizes. I'll send a new block every other week, and will give you the layout for the finished quilt at the end. I'm loving the design, and I hope you will too!
Hope you have a great weekend - we are wondering if we will get snow or not...I'll let you know!
I think if I had a day to work on any project I would work on some new designs that I've been thinking about.
The batiks are beautiful. I would love to play with them.
Beautiful batiks! If I a whole free day for any project I'd bust out my adorable hoard of llama fabric and get started cutting for my king sized south american quilt.
I have several projects going at the same time, so I would advance several of them at the same time - when I got tired of quilting the drunkard's path, I would work on either Sunshine in Winter or ATC holder, and the rail fence would be worked in as a leader/ender.
I HAVE to work on my UFO's. But I really want to start a new quilt. lol If I start a new applique project for night time, I can do both. Win, win! Do we need to sign up for the BOM or will it come with your newsletter?
To finish Quinn's quilt for graduation in June!
I would work on my "OH MY STARS" quilt.
Thank you for the giveaway. I am looking forward to your new BOM. I have really enjoyed all of them so far. Thanks for doing this for us.
Love these Island Batiks! The colors are awesome and would be perfect for a quilt I wish to make! Thanks for the giveaway.
I would love to work on something just for fun - with no deadline. Congratulations on becoming an Island Batik Ambassador.
If I had a whole day for sewing, I would get caught up on a weekly mystery quilt where I am 3 months behind.
Work on the table runner, so I will be free to pick a new project. Congratulations on the new goodies. Have fun being creative.
I need to finish my great niece wedding quilt, she got married 9/1/18, lol.
I need to quilt and bind a few of my UFOs before I start anything new, so I need to piece a backing or two.
I'm currently working on a paper piecing project - Dinner Plate Dahlia. Need every day to make progress on this ambitious project!
What gorgeous fabrics. Well, I am starting to learn applique. So, If I had a whole day, I would be doing this.
I'm making a quilt for my third great grandchild. Levi is now overdue but that works for me! I'd like to have it finished and quilted before he actually arrives. Then I will just have to make the label, bind it and pop it in the mail. Can't wait to see what you are working on.
An entire uninterrupted day for sewing/piecing/quilting would be so much fun, and productive. I have several projects in assorted stages of completion (don't we all), and my goal this year is to finish at least 12 of them for charity and 12 for personal gifting. Winning this giveaway would give me more possibilities for ideas that are percolating.
Gorgeous fabrics! Thanks for the giveaway!
I have to make quilts for 2 grandchildren and a day to do so would be perfect.
I'd like to work on the block of the month blocks I am behind with or work on a quilt I am making for my son.
I would work on a quilt for my granddaughter.
Can't wait to see the new project and the blocks.
i have a few quilts for my grandchildren that need to be finished. The blocks are done, they just need to get pieced together. Thank you for the giveaway opportunity. I am enjoying seeing what you and the other Island Batik Ambassadors create. I love batik fabric.
I checked out your etsy shop and you have some beautiful quilted things. And I enjoyed your blog also. Thats why I was led to your etsy site.
Thanks for the giveaway. If I had unlimited time I'd make a double wedding ring.
I would work on my Jinny Beyer Craftsy BOM.
I work on whatever is out & calling my name. Right now I'm working on a mini quilt I started last spring before I fractured my clavicle (which was diagnosed as shoulder impingement until I was diagnosed with Multiple Myeloma last summer)! I'm in remission now & finally feeling like sewing again!
I have many projects in the works. This week I am working on following a mystery started in the fall and my Judy Niemeyer project.
I would want to make some fabric baskets for my sewing room.
I am currently wanting to do a heart quilt and a pinwheel quilt so I would work on one of those.
I currently have a scrap quilt on my dining room table that lacks the inner border. Bought and washed the fabric yesterday so will cut and sew that next. The. Time to quilt it.


Mazda3 owners, why not speak to staff at PV Dobson and Sons about this waterproof, soft-shell Thule Ranger Box. Available for both Hatchback and Fastback models, it can be easily folded and stowed away when not in use.
For more information on stylish, practical and durable luggage solutions from Thule contact PV Dobson and Sons today.


Ontario's post-secondary institutions have ushered in free-speech policies, meeting a provincially imposed Jan. 1 deadline to tackle an issue that has polarized students in this province and beyond.
"It strikes a balance … It will give people some guidance," said Linda Franklin, president of Colleges Ontario, of the new standard policy adopted in mid-December by all publicly-funded colleges.
"It gives administrators the right to say 'We have to think about safety on campus and hate speech' " — which remains prohibited — but also doesn't silence those with opinions that are unpopular, she said.
Those on campus have to know there are "speakers that you may not like or who support your world view," but open dialogue is essential, Franklin added.
Last August, after incidents on campuses across North America where speakers faced protests, Minister of Training, Colleges and Universities Merrilee Fullerton told colleges and universities they needed to implement free-speech policies and have them in place for the New Year.
In Ontario, protests — and even arrests — have followed controversial speakers such as University of Toronto professor Jordan Peterson and Lindsay Shepherd, a Wilfrid Laurier graduate student and teaching assistant.
Shepherd was disciplined after showing her students a video of Peterson, who has gained notoriety for his public fight against the use of gender-neutral pronouns.
Institutions will be monitored and have been told they could face funding cuts for failing to comply with principles outlined by the province.
Before the holiday break, Fullerton told reporters she was pleased colleges met the deadline, and was expecting universities would as well.
"I think what (the free speech policy) will do is create some certainly around expectations, and we want to make sure that there's an environment of respect, of open debate, respectful dialogue and that's really the foundation," said Fullerton.
However, Fullerton added, the government was "constantly" hearing that free speech was being stifled on Ontario campuses.
"We heard that from students, we heard that from faculty — it was a message that we heard consistently during the campaign and after. So we know (it was an issue)," she added.
The Ontario colleges' policy — modelled on a well-regarded one developed by the University of Chicago — aims to strike a balance between promoting free speech while protecting against hate speech.
It accuses the Ford government of "trampling the democratic rights of the people of Ontario" because the policies are more aimed at avoiding protests than protecting freedom of speech.
But Franklin said a group of college leaders, as well as a representative from the College Student Alliance and legal experts, all took part in its creation, and that it will be reviewed in a year.
She said campuses around the world have dealt with protests and disruptions over speakers who students have objected to for various reasons.
The University of Toronto has a free-speech policy that has been in place for more than 25 years.
This entry was posted on Saturday, January 5th, 2019 at 12:04 pm and is filed under Education Policy Context. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.


If I ask you what the theme of your screenplay is; CAN YOU TELL ME? Believe it or not, most newbie screenwriters cannot.
That must mean theme isn't important, right?
And, if you were to read "certain" screenwriting books out there, they too might SEEM to tread in this direction but in reality, I think most just want YOU to discover or find the theme of your story without FORCING IT. Ah... There's another concept... Don't FORCE your theme on us. Why? Because if you do that and you yourself do not believe in your own theme, we can sense it from a mile away.
These elements can certainly add to the reality and believability of your characters and especially your protagonist but these elements in and of themselves do not create a theme for your story; they are simply part of your character's launching pad.
Sure, you can start out with a theme that you want to arrive at by the end of your story but what if your characters -- especially your Protagonist doesn't agree with you?
Ah... When they don't agree with you, they seem flat, unbelievable, and simply reactionary instead of having an emotional reaction to action that essentially reveals bits and pieces of their character/inner/fatal flaw and then eventually OVERCOMING their character/inner/fatal flaw. This is another reason why first drafts are so important. First and successive drafts allow you to get to know your Protagonist even better than you knew him or her after you created them on paper or in your head depending on how you work.
Pulling a theme out of a hat and then sticking it at the end of your screenplay so your Protagonist can smell his or her way toward it is fine as long as YOU'RE on board with that theme i.e., YOU believe in the truth of that theme yourself. If you don't, we'll know it because your characters and especially your Protagonist won't believe in it either.
Ah... So TRUTH is THEME. Theme is the INNER LIGHT at the end of your Protagonist's tunnel. Theme is the pot of gold at the end of the INNER RAINBOW of character. YOUR TRUTH -- not MY truth. Not your mother's truth. Just don't force it. Sure... Go ahead and PICK a theme if that's what YOU NEED to get you started.
Everybody's different. Some will know without a doubt what their theme is before even typing FADE IN while others will have their theme change over and over and over again as their Protagonist smells their way to the end of their story.
Okay, there IS A RIGHT WAY. The right way is whatever works for YOU.
Sometimes a writer doesn't even know what he or she really truly believes in until they are finished with their screenplay... Not a problem. Writing a screenplay can be just as much a road to your own self-discovery as it is for your Protagonist.
When you pull a rabbit theme out of a hat and force your story through to the end, you may just find yourself writing by the numbers but don't we really already have enough of that crap in the theaters?
Rather, consider starting out with the "rabbit theme out of the hat" just to get you moving. Just to get you writing and then, as you and your characters move through your story, you should all experience emotional reactions to action together. You should all experience new revelations and epiphanies as a TEAM.
Your characters ARE PART OF YOUR TEAM, aren't they? If not, why not? As you and your team move through your story, thematic elements will certainly pop up along the way. This is a good thing because as they pop up, you can weigh the pros and cons of these thematic elements as they relate to your Protagonist and supporting characters. Thematic elements will help tweak your Protagonist's tunnel so that by the time he or she gets to the end of it, your story not only entertains us but reveals a reason and a purpose for your Protagonist's tunnel in the first place i.e., we walk away caring about your movie instead of telling our family, friends, and associates NOT TO BOTHER.
If you're perceptive about your characters and story, you'll see these thematic elements pop up and maybe even explore them. Some may lead nowhere, others may carve your way through to your overall theme.
Plot points 1, 2, 3, 4, 5, etc.
It's not that these elements can't be written in stone, they can. And it's certainly okay to plan all these out ahead of time -- in fact, it's recommended but these acts and plot points are simply the OUTER MARKERS of your story. Part of your structure. A way for your team to get from POINT A to POINT B and then on to POINT C, etc.
Thematic elements are the INNER MARKERS of your story. As your team follows your road map via the outer markers, they go up against the obstacles you've laid in front of them but remember, your team is made up of HYPER-REAL characters. They don't simply bump into an obstacle and get pushed into another direction. They go through the process of EMOTIONAL REACTION TO ACTION each time they bump into one of these obstacles.
However, in the beginning of your story, your team's (and especially your Protagonist's) emotional reaction to action(s) is/are based on his or her character/inner/fatal flaw and the decisions made by your Protagonist, based on their character/inner/fatal flaw serves to peel back those layers of your Protagonist's personality.
So once you KNOW YOUR THEME, you can now begin to tweak those series of decisions so that by the end of your story, your Protagonist's transformational character arc is complete.
Act 1: Get your Protagonist up a tree.
Act 2: Throw rocks at your Protagonist.
Act 3: Get your Protagonist down out of the tree.
Your Protagonist's emotional reaction to action is what's been keeping him or her from growing as a human being. They are stuck in their own muck. They always do things the hard way which is THEIR way. Their way never really works and often causes a lot more trouble than it's worth. When the inciting incident comes along, their emotional reaction to action is to do things the same old way they've always done them before but it doesn't work out the way they want and actually causes them even more grief... More conflict. More tension and hence, they are now UP A TREE.
Now that your Protagonist is up the tree, he or she has to figure out a way to get down. If he or she keeps doing things the same old screwed-up way they've been doing things for so long, they AIN'T gettin' down outta that tree, are they? Hell no! Yet, he or she keeps making the SAME KIND of decisions to get outta that tree and eventually... about halfway through Act 2, he or she finally realizes IT AIN'T WORKIN'! Of course, each decision or emotional reaction to action PRIOR to this epiphany has slowly but surely led the Protagonist to learn that he or she is missing something.
He or she is now beginning to realize that maybe the way they've been doing things (living life) really isn't working after all i.e., their character/inner/fatal flaw keeps them from getting down outta the tree. This is where YOUR TRUTH begins to light up the end of your Protagonist's tunnel and they start to see the glimmer of that light and just like we all tend to do, your Protagonist decides to struggle TOWARD the LIGHT. He or she now has a PURPOSE that should reveal itself by the end of the movie.
Your Protagonist has finally figured out that he or she needs to adopt a new strategy for LIFE. He or she has more or less figured out that in order to get outta that tree, they have to do something that they've never done before. Something new... Something different. Let's say they used to JUMP outta the tree before and every time he or she did that, they broke some bones (metaphorically speaking). So now, your Protagonist has finally figured out that JUMPING just makes matters worse... i.e., there's gotta be a better way and now's the time to figure out what that better way is.
They make a new plan and try to work the plan as they struggle toward the light and if the plan works, they will make it to the end of the tunnel. Of course, this is where you throw in the worst of the obstacles but NOW, because your Protagonist is becoming a new person, has gained newfound strength, he or she is able to thwart the obstacles (barely) to eventually climb down outta that tree... TRANSFORMED.
In other words, the obstacles that you throw in your Protagonist's way should play on his or her character/inner/fatal flaw(s). Your Protagonist should eventually achieve not only their OUTER, PHYSICAL, TANGIBLE GOAL but overcome his or her INNER OBSTACLES to complete their transformation AND achieve their outer goal.
These are the kinds of stories that resonate with an audience. Meaning that your Protagonist achieves more and more internal strength as he or she overcomes the external obstacles that you throw in their way. One should not exist without the other and it's this parallel struggle and eventual achievement of BOTH that the audience ABSORBS and walks away happy and ready to recommend the movie to others.
Coming up with your THEME, either before, during, or after your first draft, allows you to go back to your characters, action, and dialogue and flesh in more SUBSTANCE that supports your theme every step of the way throughout your story.
Theme can be complex or simple... Again, there are no rules but keep in mind the audience you're writing your screenplay for. Will they understand a complex theme? Will they absorb it? Will they GET IT? The simpler the theme, the wider the audience, the better the absorption and understanding.
The theme is the meaning of your Protagonist's journey and generally speaking, to make things just a little easier -- NOT FORMULAIC -- NOT BY THE NUMBERS -- consider beginning your Protagonist's journey 180 degrees or as opposite as you can make it from their ultimate ending and transformation.
For instance, if the theme of your story is that PEOPLE SHOULD FACE THEIR FEARS, why not start off with your Protagonist always running away from his fears and problems. You know that the transformational character arc you want for your Protagonist is for him or her to be able to face their worst fears by the end of the story so start them off by running away from just about everything because that's what they've always done. Make them run away in their emotional reaction to action and dialogue and slowly but surely steer them toward your overall theme.
You may find a new theme by the time you get to the end or you might just wind up with a great story that needs nothing added but use THEME as your guiding light. If you do come up with a new theme, that's what rewrites are for… i.e., fleshing in substance that supports your theme.