Home » Blog » Before You Sell » Downsizing a Home Comes With Challenges – 4 Tips That Make It Easy!
The art of minimalism has been recognized in Eastern countries for ages, but it's becoming more and more popular in the West as time presses on. As you can imagine, just by looking at the title of the movement, Minimalism is pretty simple.
Clean your house — your whole house, crazy spring-cleaning style — and touch every single item you own. This includes furniture, knick-knacks, decorations, keepsakes — all of them.
Then ask yourself one very simple question: "Does this item bring me joy?" Really think about it. Don't ask, "Will this item bring me joy one day?" or "Has this item brought me joy before?" Ask, "Does it bring me joy at this time in my life?" If the answer is no, get rid of it. Recycle it. Donate it. Sell it. Dismiss it from your life. You'll be surprised how easy downsizing can be if you look at things in this way.
OK, we know this sounds silly, but hear us out: It's easy to realize how much you need — or more to the point, don't need — something if you don't have it. If you spend a weekend in the woods roughing it, you'll get a different perspective on your belongings. You might find that you return to your home and realize that you're living in a state of excess, and you'll be able to get rid of many more items than you might have previously imagined.
After finding a home that meets your basic needs and your budget, measure your new room sizes. When you understand how much physical room you have, you'll be able to make decisions more easily about what you should throw away and what you should keep. You might have a 20-by-15-foot living room right now, but your new home only has a 12-by-12-foot living room. When you measure, you'll know you probably have to get rid of that antique lounger you never sit on and that bulky hutch that's just for show.
This tip concerns your keepsakes, collectibles and photographs in particular. These are items that may bring you joy, but they're also things that take up a lot of space when they don't have to. Scan your pictures onto your computer so you can look at them anytime without having to rummage through clunky, old photo albums. Take pictures of your larger keepsakes and upload them instantly to your phone or computer.


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Richard is a UTS Luminarie and a Fellow of UTS.
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text:
\subsection{Experiment setup}

To demonstrate our approach, we consider a range
of physics-based tasks - two contextual bandit tasks and three continuous
control RL tasks. For continuous control tasks, we adopt three MuJoCo
tasks: Half Cheetah-v1, Hopper-v1 and Walker2D-v1 \cite{openai-gym}, implemented using MuJuCo physics simulator \cite{todorov2012mujoco}.
For contextual bandit tasks with discrete actions,
we utilise two tasks named \emph{Bowling}
and \emph{Basketball} (Fig. \ref{fig:contextual-bandit}) implemented
with the Pymunk physics simulator \cite{pymunk}. \emph{Bowling} is derived
from 0000 PHYRE \cite{Bakhtin2019-dq} template, and consists of 3 balls, where the goal is to keep two of the balls (blue and green) in contact
by controlling the position and size of the red ball (i.e. action)
(Fig. \ref{fig:Bowling}) and dropping it on the blue ball. In \emph{Basketball}, the aim is to bounce a ball into a basket using an angled plank (Fig. \ref{fig:Basketball}).

For the purpose of this study, we emulate the real-world through the same simulator but with an added damping factor (i.e., a drag force).
To evaluate the performance of policies in contextual
bandit tasks, we use the \emph{AUCCESS} score from PHYRE, which is a metric designed to measure both the sample efficiency, as well as the reward obtained. It is defined
as follows: given attempts $k\in\lbrace1,..,100\rbrace$
at solving a given task, weights $w_{k}$ is determined as $w_{k}=log(k+1)-log(k)$,
then the \emph{AUCCESS }score is $\sum_{k}w_{k}.s_{k}/\sum_{k}w_{k}$
where $s_{k}$ is the success percentage at $k$ attempts \cite[Sec. 3.2]{Bakhtin2019-dq}.

\noindent\textbf{Baselines:} The baselines considered in all our experiments include learning without a prior (\emph{No Prior}), and a  Domain Randomisation (\emph{DR}) baseline, where an agent learns a policy on randomised latent factors. Additionally, for MuJoCo tasks we compare against \emph{MAML}\cite{finn2017model} as a meta-learning baseline where a set of randomised latent factors is used as tasks to train a fast adaptable meta-objective. For the contextual bandit tasks, we also consider an \emph{Estimated} baseline, where the latent factor values are estimated by minimising the differences in the trajectories of the real and simulation environments. To compensate for real-world interactions of the policy search, non-BO baselines (\emph{DR}, \emph{MAML} and \emph{Estimated}) are additionally trained for an equivalent number of interactions as the policy search in the real-world.

\subsection{UPN Based Policy Search with Bayesian Optimisation}

For the contextual bandit setting, we use a DQN based UPN implementation to find policies for our contextual
bandit tasks. To train UPNs for continuous
action tasks, we adopt the UPN implemention\footnote{https://github.com/VincentYu68/policy\_transfer}
of \cite{yu2018policy}, on which a policy is learnt using Proximal Policy Optimization the (PPO)
algorithm \cite{DBLP:journals/corr/SchulmanWDRK17}. We train our
three MuJoCo tasks for $2\times10^{8}$ steps on this UPN implementation
and build separate UPNs for each task, on which we conduct our policy
search process to find a good policy for a given environment.

Given a trained UPN and an environment with unknown latent factors,
we execute 20 iterations of Bayesian optimisation (BO) to search
for a good policy for the environment. To provide a stable optimisation
objective for BO in the MuJoCo environments, we use the sum of rewards
over a window of 500 interaction steps as the optimisation
objective. Furthermore, to isolate the effects of latent factors, we fix all other influencing
factors during a BO trial by initialising each iteration
from a fixed initial state. After BO policy search, the best estimated latent factor value is used to condition the UPN to fetch the policy, which is transferred to the real environment to evaluate the jump start performance (i.e., cumulative reward of the transferred policy) over 100 random episodes with a 500 step horizon.

For contextual bandit tasks, we maintain three separate folds of task sets
for training, validation and testing purposes. For these tasks, we conduct BO search on the DQN based
UPN using the validation fold of datasets, with the objective set to maximise the reward over tasks in the fold. For each
BO evaluation, the total number of interactions is the number of actions attempted $\times$ number of tasks in the validation fold.

\subsection{Evaluation of the \emph{Policy Prior}}

\begin{figure*}
\centering{}
\subfloat[Hopper-2Dim]{\includegraphics[width=0.23\paperwidth]{imgs/2D/hopper-2d-500win.png}
}
\subfloat[Walker2D-5Dim]{\includegraphics[width=0.23\paperwidth]{imgs/5D/walker-5d-500win.png}
}
\subfloat[Half-Cheetah-5Dim]{\includegraphics[width=0.23\paperwidth]{imgs/5D/cheetah-5d-500win}
}
\caption{\label{fig:gym-2d}\label{fig:gym-5d}Performance (cumulative reward) using a BO policy search with \emph{Policy prior} on three MuJoCo tasks a) when there are \textbf{two} (friction and mass), and c, d) \textbf{five} (friction, restitution and mass of 3 body parts) unknown latent parameters of Hopper, Walker2D and Half-Cheetah. For all tasks a 500 step horizon is used.}
\end{figure*}

Fig. \ref{fig:gym-2d} and Table \ref{table:results-mujoco} illustrate the sample
efficiency of the \emph{policy prior} introduced in Section \ref{subsec:Building-the-gp-prior}
when integrated with BO policy search, evaluated on the three MuJoCo environments. For each environment we consider two settings with two (2 Dim) and five (5 Dim) unknown physics latent factors are present in the real-world, which demonstrate the efficacy
of our method in both low and moderately high dimensional latent factor space.

When building the priors for these environments, in the 2 Dim setting
we uniformly sample $100$ latent factors while in 5 Dim
setting $250$ samples are used. We then evaluate the performance
of UPN policies fetched at each sampled latent value on $10$ and $250$ different simulated
environments (i.e., environments with different true latent values) for 2 Dim and 5 Dim settings, respectively.
For both Bowling and Basketball tasks, we follow the same procedure to build priors for the contextual bandit agent, using $100$ latent factor combinations drawn by discretizing the latent space uniformly.

\subsubsection*{Evaluation using low cost fidelities}

\begin{table*}
\begin{centering}
\subfloat[\label{table:results-mujoco} Jump start performance (cumulative episodic reward) with \emph{Policy prior} on
three MuJoCo tasks when there are two (2 Dim) and five (5 Dim) unknown
latent parameters. For all tasks a 500 step horizon is used and jump start
is averaged over 100 random episodes.]{\centering{}%
\begin{tabular}{|c|c|c|c|c|}
\hline
 & MAML & DR & \emph{No Prior} & \emph{Policy Prior}\tabularnewline
\hline
\hline
Walker2D - 2 Dim & 514.70 $\pm$ 1.41 & 1530.18 $\pm$ 23.56 & 1353.82 $\pm$ 6.32 & \textbf{1602.9 $\pm$ 6.54}\tabularnewline
\hline
Walker2D - 5 Dim & 552.24 $\pm$ 15.30 & 941.88 $\pm$ 18.17 & 1146.98 $\pm$ 6.00 & \textbf{1186.76 $\pm$ 6.53}\tabularnewline
\hline
Half-Cheetah - 2 Dim & 592.76 $\pm$ 39.81 & \textbf{4581.58 $\pm$ 22.38} & 3986.11 $\pm$ 9.39 & 4050.92 $\pm$ 6.07\tabularnewline
\hline
Half-Cheetah - 5 Dim & 375.54 $\pm$ 3.85 & 1784.36 $\pm$ 9.33 & 2261.26 $\pm$ 11.1 & \textbf{2310.88 $\pm$ 7.80}\tabularnewline
\hline
Hopper - 2 Dim & 394.01 $\pm$ 1.23 & 1603.51 $\pm$ 10.32 & \textbf{1930.11 $\pm$ 6.32} & \textbf{1928.74 $\pm$ 5.46}\tabularnewline
\hline
Hopper - 5 Dim & 658.21 $\pm$ 3.69 & 1322.75 $\pm$ 5.90 & 1519.73 $\pm$ 5.06 & \textbf{1541.45 $\pm$ 3.96}\tabularnewline
\hline
\end{tabular}}

\subfloat[\label{table:results-bandits} Jump start performance (\emph{AUCCESS} score) of bandit tasks. Policy search process has been conducted using low fidelity actions, 20 for Bowling and 5 for basketball.]{\centering{}%
\begin{tabular}{|c|c|c|c|c|}
\hline
 & DR & Estimated & \emph{No Prior} & \emph{Policy Prior}\tabularnewline
\hline
\hline
Bowling & 0.8953$\pm$ 0.0067 & 0.8956$\pm$ 0.0108 & 0.8985$\pm$ 0.0040 & \textbf{0.9202$\pm$ 0.0053}\tabularnewline
\hline
Basketball & 0.7390 $\pm$ 0.0723 & 0.3145 $\pm$ 0.1147 & \textbf{0.8784 $\pm$ 0.0827} & \textbf{0.9519 $\pm$ 0.011}\tabularnewline
\hline
\end{tabular}}

\caption{\label{table:results-all}\emph{Policy Prior's} initial transfer policy performance (jump start) on MuJoCo and Bandit tasks compared with baselines. Bold represents the best performance.}
\par\end{centering}
\end{table*}

One of the key issues with UPN based online system identification
is the evaluation cost during the policy search process. For instance, if
we follow the PHYRE formulation for Bowling task which evaluates 100 actions per task,
it would cost 40,000 interactions with the real-world to evaluate
20 BO iterations using a validation set of 20 tasks (100 actions $\times$
20 tasks $\times$ 20 iterations). To improve the sample efficiency
of this policy search process, we examine using a low fidelity evaluation by choosing the best 20 actions based on their associated rewards obtained from rollouts carried out in simulation.
Following the policy search with the policy prior, we evaluate the performance of the latent conditioned policy using 100 best ranked actions on the test set (Table. \ref{table:results-bandits}). Similar evaluations are carried out for the Basketball task using 5 best actions for policy search and jump start evaluated using the best 100 actions.

We designed the Basketball task such that coefficient of restitution (CoR) of objects are more impactful for the task's goal than friction. For example, when the CoR of the ball is approximately less than 0.3, the task cannot be achieved irrespective of the friction value. We found that the Gaussianity checking step of the \emph{Policy prior} (Sec. \ref{subsec:gaussianity-check}) is particularly useful to address such non-Gaussian settings (refer appendix for further details).

From the results in Fig. \ref{fig:gym-5d} and Table \ref{table:results-all}, it is clear that considering the superior sample efficiency offered through policy search, combined with the consistently good jump start performances, the prior policy is a beneficial tool for efficient learning in the parameterisable environments considered in this work. For low dimensional cases (e.g. 2 Dim), DR performs on par or better than BO methods, likely due to the low randomness offered by the environment. However, when the number of unknown parameters increases, DR performs poorer than policy search methods.

\begin{comment}
\begin{figure*}
\begin{centering}
\subfloat[Bowling-BO\label{fig:phyre-bo-action5}]{\includegraphics[width=0.20\paperwidth]{imgs/Phyre/prior2/phyre-20a-colour-2.png}

}\qquad{}\subfloat[Bowling-jumpstart\label{fig:phyre-jump-start-action5}]{\includegraphics[clip,width=0.17\paperwidth]{imgs/Phyre/prior2/jump-start-20a-colour-2.png}
}
\subfloat[Basketball-BO\label{fig:gaussian-check-bb}]{\includegraphics[width=0.20\paperwidth]{imgs/BB/bb-guassian-filter-siglevel01-standardised-noprior2-BO-action5-colour-2.png}

}\qquad{}\subfloat[Basketball-jumpstart]{\includegraphics[width=0.17\paperwidth]{imgs/BB/bb-guassian-filter-siglevel01-standardised-noprior2-jump-start-action5-after800trans-colour-2.png}
}
\par\end{centering}
\caption{\label{fig:low-fidelity}\emph{Policy Prior's} policy search and jump
start performances using a low fidelity set for bandit tasks, (a-b) Bowling
task with 20 best actions and (c-d) Basketball task with 5 best actions.
a,c) BO policy search process for \emph{Policy Prior} and \emph{No prior} on
the validation set. b,d) Jump start performance of \emph{Policy Prior}
compared against the baselines on test set.
}
\end{figure*}
\end{comment}

\begin{comment}
\begin{figure*}
\centering{}
\subfloat[2Dim-Walker2D]{\includegraphics[width=0.23\paperwidth]{imgs/2D/walker-2d-500win.png}

}\subfloat[]{\includegraphics[width=0.13\paperwidth]{imgs/2D/jumpstarts/transfer-walker-2d-500win.png}
}\subfloat[5Dim-Walker2D]{\includegraphics[width=0.23\paperwidth]{imgs/5D/walker-5d-500win.png}

}\subfloat[]{\includegraphics[width=0.13\paperwidth]{imgs/5D/jumpstart/transfer-walker-5d-500win.png}

}\medskip{}
\subfloat[2Dim-Half-Cheetah]{\includegraphics[width=0.23\paperwidth]{imgs/2D/cheetah-2d-500win.png}
}
\subfloat[]{\includegraphics[width=0.13\paperwidth]{imgs/2D/jumpstarts/transfer-cheetah-2d-500win.png}
}
\subfloat[5Dim-Half-Cheetah]{\includegraphics[width=0.23\paperwidth]{imgs/5D/cheetah-5d-500win}
}
\subfloat[]{\includegraphics[width=0.13\paperwidth]{imgs/5D/jumpstart/transfer-cheetah-5d-500win.png}

}\medskip{}
\subfloat[2Dim-Hopper]{\includegraphics[width=0.23\paperwidth]{imgs/2D/hopper-2d-500win.png}
}\subfloat[]{\includegraphics[width=0.13\paperwidth]{imgs/2D/jumpstarts/transfer-hopper-2d-500win.png}
}
\subfloat[5Dim-Hopper]{\includegraphics[width=0.23\paperwidth]{imgs/5D/hopper-5d-500win.png}
}\subfloat[]{\includegraphics[width=0.13\paperwidth]{imgs/5D/jumpstart/transfer-hopper-5d-500win.png}
}
\caption{\label{fig:gym-2d}\label{fig:gym-5d}Performance in terms of the cumulative reward using a Bayesian Optimisation policy search with \emph{Policy prior} on three MuJoCo tasks and their respective jump start performances,
a-b),e-f),i-j) when there are \textbf{two} unknown latent parameters,
mass and friction in the real world, and c-d),g-h),k-l) when there
are \textbf{five} unknown latent parameters: friction, restitution and mass of 3 body parts
Hopper, Walker2D and Half-Cheetah. For all tasks a 500 step horizon is used and jump start is averaged over 100 random episodes.
All \emph{Policy Prior}, \emph{No prior} and \emph{DR} are trained for $2\times10^{8}$ steps in a simulated environment.}
\end{figure*}
\end{comment}

\begin{comment}

\begin{table}[h!]
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Dim \textbackslash \ Environment} & \textbf{Walker} & \textbf{Cheetah} & \textbf{Hopper}\tabularnewline
\hline
\hline
\textbf{2D} & 514.70$\pm$1.41 & 592.76$\pm$39.81 & 394.01$\pm$1.23\tabularnewline
\hline
\textbf{5D} & 552.24$\pm$15.30 & 375.54$\pm$3.85 & 658.21$\pm$3.69\tabularnewline
\hline
\end{tabular}\caption{\label{table:gym-maml} Performance of \emph{MAML} baseline in terms of the cumulative reward over 500 step window for three MuJoCo environments and two unknown latent factor settings. Tasks are meta-trained for $2\times10^{8}$ steps in a simulated environment.}
\end{table}

\subsubsection*{Implementation Details}

When building BO based policy search, we used GPy\cite{gpy2014} for GP model building
and Emukit \cite{emukit2019} for BO process.
All \emph{Policy Prior} and \emph{No Prior} results reported are averaged over 5 trials, each
of which is initialised with its random
number generator seed mutually exclusively set to one of 50, 100,
150, 500 and 1000. For all BO operations, we use RBF kernel, Expected
improvement (EI) as the acquisition function, with LBFGS as the optimiser.
For no prior baseline, the process starts
with 3 random samples and proceeds to build the GP. In MuJoCo tasks,
BO samples within the bounds {[}0, 1{]}, as all features are normalised.
For bandit tasks, friction range is {[}0, 3{]} while CoR is {[}0,
1{]}.

For MuJoCo tasks, depending on the setting, we set the ground truth to a vector of length 2 or 5, generated with random number generator seed set to 1. To separate the real-world from the simulated environment, we use a damping value of 3 using \emph{PyDart} simulator's  \emph{set\_damping\_coefficient} API call.

Contextual bandit tasks maintain standard three folds of datasets
and a maximum action size to sample from. The Bowling task uses 5000
actions, with 100 tasks in total split to 60 training, 20 validation
and 20 test tasks. The Basketball task utilises
40,000 actions, with a dataset containing 25 tasks, split into 15
training, 5 validation and 5 test sets. When setting up the real-world,
for both tasks a damping of 0.8 is used whereas in the simulated environment
no damping value is used.

All baselines in Fig.\ref{fig:low-fidelity} are generated as follows:
DR is a domain randomised baseline, in which friction [0, 3] and CoR [0, 1] were
sampled to train an RL agent. True latent is a policy trained with a DQN using the actual
latent values in the real-world, and "Estimated" estimates latent
values by minimising the displacement difference
between sampled real-world trajectories and their corresponding
simulated trajectories caused by sampled latent values using cross-entropy
method. For this purpose, 6,000 real-world interactions for Bowling task and
1,500 for Basketball have been used. These interactions have not been accounted for in the plots, and in reality, these baselines use upto 6000 and 1500 interactions more.
All baselines are pre-trained for an equal number of steps
as UPN in the simulated environment and then trained for 8000 real-world interactions
for Bowling task and 800 interactions for Basketball task to match UPN's
BO search budget of 8000 and 500 interactions, respectively for two tasks.

\end{comment}

\section{Introduction\label{sec:intro}}

\input{intro_lk.tex}

\section{Methodology\label{sec:method}}

\input{method.tex}

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

\input{exp.tex}

\section{Related Work\label{sec:bg}}

\input{bg.tex}

\section{Conclusion\label{sec:conclusion}}

\input{conclude.tex}

{\small{}\bibliographystyle{IEEEtran}

\subsection{UPN Based Policy Search\label{subsec:Latent-Factor-Estimation}}

To formulate the UPN, we augment the task's state $s_{t}\in S$ as
$s_{t}=[o_{t},\theta]$, where $o_{t}$ is the task's observable state
at time step $t$, and $\theta$
is the set of latent factors for the given episode (we assume, $\theta$ is
fixed for a given episode throughout this study). Even though UPNs
have been implemented with policy gradient algorithms for mainly continuous
action tasks \cite{up-net,yu2018policy}, in this study we also design
a DQN based UPN for discrete action tasks such as bandit tasks.
However, to examine the performance of \emph{Policy
prior} for continuous action tasks, we adopt the existing policy gradient
based UPN. When training the UPN, we only use simulation, making it
inexpensive and practical.
\begin{comment}
(Appendix \ref{sec:Appendix-1})
Given that DQNs have demonstrated to be more sample efficient in many
tasks when compared with policy gradient based methods\cite{dqn-vs-pg},
a DQN based UPN can improve the sample efficiency of real-world transfer
for discrete action tasks.
\end{comment}

Once we obtain a trained UPN, when conditioned by the latent factor
estimate $\theta,$ it will produce a policy $\pi(s_{t}=[o_{t},\theta])$
for the time step $t$. We then evaluate this policy on the real-world
task set $W$, and measure its performance (episodic return) $G_{W}(\pi)$. We use this measure as the
optimisation objective for BO to find $\theta^{*}$ such that,

\begin{equation}
\theta^{*}=\underset{\theta}{argmax}\:G_{W}(\pi(s_{t}))\;\mid s_{t}=[o_{t},\theta]\label{eq:5}
\end{equation}

\subsection{\label{subsec:Building-the-gp-prior} Policy Prior}

To build the \emph{Policy prior}, we first uniformly sample $N$ latent
factors and retrieve policies $\pi_{n=1,..,N}$ for each corresponding
$X_{n=1,..N}$ latent factors by concatenating $X_n$ with task observation state at
the given time-step and using this augmented state to fetch a policy from the UPN (i.e., conditioning).
Secondly, we define an environment setting $e$ as a simulation setting with some
fixed latent factor configuration (i.e., $\psi_\theta$ for some $\theta$), and we select a set of uniformly sampled
environment settings $E$ with $e \in E$ from defined bounds for the
latent factors. To get a measure of the behaviour of $\pi_{n=1..N}$
policies in these $E$ settings, we evaluate each of $\pi_{n}$
on all $E$ simulated settings and record their performances. Subsequently,
we calculate the distribution statistics,
mean $\mu_{n=1,..,N}$ and variance $\sigma_{n=1,..,N}^{2}$ of jump
starts for $E$ evaluations of each $\pi_{n}$.

\begin{algorithm}[t]
\caption{Policy Prior Formation $(policy\_prior)$}
\label{alg:physics_prior} \hspace*{\algorithmicindent}
\textbf{Input:} \begin{algorithmic}[1]
\State $U$ - a simulator trained Universal Policy Network
\State $\xi \in W$ - a set of tasks
\State $\psi$ - a simulated environment
\State $N$ - Number of observations to sample
\State $e \in E$ - Environment settings with different ground truth latent values
\State $B_l$ - Bounds for latent factors $l$, $l \in \{1..L\}$
\State $\gamma$ - Minimum threshold of P-value for Gaussianity of sampled points

\State \textbf{Output:} $X_{1..N}$ latent factor samples, $\mu_{1,..N}$, $\sigma^2_{1,..N}$ mean and variance of UPN policies evaluated in $E$ environments

\State //Sample latent values
\For{$l \gets 1$ to $L$}
\State $X_l \gets$ Generate $N$ uniform distributed observations in range $B_l$
\EndFor

\State //Evaluate sampled latent values across environments
\For{$n \gets 1$ to $N$}
	\State $\pi_n \gets U$ conditioned policy at $X_{n}$
	\For{$e \gets 1$ to $E$}
		\State $Y_{n, e} \gets$ $\pi_n$ evaluated in setting $e$ of simulator $\psi$ on task set $W$
	\EndFor
	\State $\mu_n \gets mean(Y_{n,1..E})$
	\State $\sigma^2_n \gets variance(Y_{n,1..E})$
	\State //Call Kolmogorov-Smirnov test function \cite{ktest} to calculate the p-value of $X_n$ sample for Gaussianity check. Sec. \ref{subsec:gaussianity-check}
	\State $p\_val_n = kstest(Y_{n,e=1..E}, \mu_n, \sigma^2_n)$
	\If {$p\_val_n < \gamma$}
		\State Remove $X_n$
	\EndIf
\EndFor

\end{algorithmic}
\end{algorithm}

\begin{algorithm}[t]
\caption{Physics aware BO Policy Search}
\label{alg:policy_search} \hspace*{\algorithmicindent}
\textbf{Input:} \begin{algorithmic}[1]
\State $T$ - Number of BO iterations to run, $k$ - GP kernel, $\phi$ - GP initial hyperparameters, $\alpha$ - acquisition function, $\psi_r$ - real world, $W$ - task set to evaluate

\State \textbf{Output:} $x^*_{i=1..T}$, $y^*_{i=1..T}$ BO estimated new latent factors and corresponding real-world jump start performances\\
\State //Get Policy prior latent samples, mean and variance
\State $X_{1..N}$, $\mu_{1..N}, \sigma^2_{1..N} \gets policy\_prior$
\State //Construct covariance matrix using kernel k
\State $K \gets k(X_{1..N}, X_{1..N};\phi)$
\State //Add Policy prior variance as noise variance to Kernel matrix diagonal entries + $\epsilon$ sim-to-real noise. Eq. \ref{eqn:gp_prior}.
\For{$i \gets 1$ to $N$} $K_{i,i} \gets K_{i,i} + \sigma^2_i + \epsilon$ \EndFor
\State //Build the Policy prior integrated Gaussian process
\State $GP_{p} \gets GP(\mu_{1..N}, K)$

\State //Run the BO policy search for T iterations
\For{$i \gets 1$ to $T$}
\State //acquisition function provides the next most likely latent factors to improve the optimisation objective
\State $x^*_i \gets \alpha(GP_{p})$
\State // UPN conditioned policy at the latent factors suggested by acquisition function
\State $\pi_i \gets$ $U$ conditioned by $x^*_i$
\State $y^*_i \gets$ $\pi_i$ evaluated on task set $W$ in real-world $\psi_r$
\State $GP_{p} \gets GP_{p} \cup (x^*_i, y^*_i)$
\EndFor
\end{algorithmic}
\end{algorithm}

\subsubsection{\label{subsec:gaussianity-check}Checking for Gaussianity of Prior}
In the BO process, a Gaussian process is used as the model of the
function. As per the modelling assumption of the Gaussian process,
each observation should strictly follow a Gaussian distribution. Any
mismatch between this assumption and reality will make the resultant
model inconsistent and BO can get affected. Therefore, as an additional
step, we verify whether the prior is distributed normally using Kolmogorov-Smirnov
Goodness-of-Fit Test \cite{NIST-handbook}. For this purpose, we
iterate through all sampled latent values in the \emph{Policy} \emph{prior}
to verify whether their p-values are within a defined threshold, and
if they are not, the respective points are dropped from the prior.

\subsubsection{\label{subsec:bo-transfer} Knowledge Transfer with BO}
To use these synthetic experiences
as prior observations in the GP and then use it in the BO process we use the
transfer BO algorithm of \cite{joy2019flexible}. In \cite{joy2019flexible}, the transfer
was done from a set of source observations to a target optimization problem.
In our case, the source observations are the synthetic observations
and the target observations come from the given real-world environment.
With that, the synthetic observations now can be included just
as a set of extra observations with added noise. The noise allows
the GP to adjust for the expected difference of performance in the real-world,
compared to the simulation world.
This prior augmented GP's observation data at time $t$ of the optimization can be denoted
as $D_t$ = $\{X_n, \mu_n, \sigma^2_n\}_{n=1}^N \cup \{X_i^*, y_i^*\}_{i=1}^t$, where $\{X_i^*, y_i^*\}$
are real-world observations. This results in the kernel matrix form as:

\begin{equation}
\label{eqn:gp_prior}
  K_{*} = K + \begin{bmatrix}\begin{bmatrix}\sigma_{1} &  & 0\\
 					& ..\\
					0 &  & \sigma_{N}
					\end{bmatrix}_{N\times N} \\
 					& \sigma_{r}^{2}I_{t\times t}
				\end{bmatrix}
\end{equation}

where $K$ is the covariance matrix for $\{X_{n=1}^N \cup X^*_{i=1..t}\}$, and $\sigma^2_{r} \in \mathbb{R}$ is the noise variance for real-world observations.

The posterior is computed based on this $K_*$ and $D_t$. We choose this transfer learning BO algorithm because
of its favourable convergence property that shows the convergence is no worse than a
standard BO without prior observations (3.1, Theorem 2 of \cite{joy2019flexible}).

The process of building \emph{Policy prior} is detailed in Algorithm \ref{alg:physics_prior},
while the overall \emph{Policy prior} integrated BO policy search
is explained in Algorithm \ref{alg:policy_search}.

\subsection{Policy Prior Integrated Policy Search Performances - Extended}

Fig. \ref{fig:gym-2d} shows the performance of \emph{Policy prior} integrated Bayesian Optimisation policy search, in terms of the cumulative reward, on three MuJoCo tasks, Hopper, Walker2D and Half-Cheetah. Fig. \ref{fig:low-fidelity} shows the \emph{Policy prior}'s policy search performance on two bandit tasks, Bowling and Basketball, in terms of \emph{AUCCESS} score \cite[Sec. 3.2]{Bakhtin2019-dq}.

\begin{figure*}
\centering{}
\subfloat[2Dim-Walker2D]{\includegraphics[width=0.30\paperwidth]{imgs/2D/walker-2d-500win.png}
}
\subfloat[5Dim-Walker2D]{\includegraphics[width=0.30\paperwidth]{imgs/5D/walker-5d-500win.png}
}
\medskip{}
\subfloat[2Dim-Half-Cheetah]{\includegraphics[width=0.30\paperwidth]{imgs/2D/cheetah-2d-500win.png}
}
\subfloat[5Dim-Half-Cheetah]{\includegraphics[width=0.30\paperwidth]{imgs/5D/cheetah-5d-500win}
}

\medskip{}

\subfloat[2Dim-Hopper]{\includegraphics[width=0.30\paperwidth]{imgs/2D/hopper-2d-500win.png}
}
\subfloat[5Dim-Hopper]{\includegraphics[width=0.30\paperwidth]{imgs/5D/hopper-5d-500win.png}
}

\caption{\label{fig:gym-2d} Performance in terms of the cumulative reward using a Bayesian Optimisation policy search with \emph{Policy prior} on three MuJoCo tasks, a,c,e) when there are \textbf{two} unknown latent parameters,
mass and friction in the real world, and b,d,f) when there are \textbf{five} unknown latent parameters: friction, restitution and mass of 3 body parts
Hopper, Walker2D and Half-Cheetah. For all tasks a 500 step horizon is used.}
\end{figure*}

\begin{figure*}
\begin{centering}
\subfloat[Bowling\label{fig:phyre-bo-action5}]{\includegraphics[width=0.30\paperwidth]{imgs/Phyre/prior2/phyre-20a-colour-2.png}
}\qquad{}
\subfloat[Basketball\label{fig:gaussian-check-bb}]{\includegraphics[width=0.30\paperwidth]{imgs/BB/bb-guassian-filter-siglevel01-standardised-noprior2-BO-action5-colour-2.png}
}\qquad{}
\par\end{centering}
\caption{\label{fig:low-fidelity}\emph{Policy Prior's} policy search performances in terms of the \emph{AUCCESS} score, on the validation task set. Policy search has been conducted using a low fidelity action set for bandit tasks, (a) Bowling task with 20 best actions and (b) Basketball task with 5 best actions.
}
\end{figure*}

\subsection{Ablation Study: Checking for Gaussianity of Prior in Basketball Task}

Basketball task is designed such that CoR of objects are more impactful
for the task's goal than friction. This is elucidated in Fig. \ref{fig:mean-basketball-9}
and \ref{fig:variance-basketball-9}, which show the mean and variance
of jump start performances of basketball UPN policies, when conditioned
at 10$\times$10 grid of latent values and then transferred to 9 different
ground truth settings. In essence, when the CoR of the ball is approximately
less than 0.3, the task can not be achieved irrespective of the friction
value. We utilise this skewed setting to
investigate the behaviour of our \emph{Policy
prior} in tasks that do not respond equally to all latent factors
involved.

Fig. \ref{fig:bo-converg-bb-no-filter} shows that during BO policy search,
\emph{Policy Prior} without the Gaussianity check performing
worse than the no prior baseline on the Basketball task. We posit this is due to the non-Gaussian
distribution of the \emph{Policy Prior} and to remedy this issue, we filter out non-Gaussian
observations with p-value less than 0.1 using Kolmogorov-Smirnov Goodness-of-Fit
Test. In the best 5 action setting of the basketball task we are using,
it filters out 59 observations out of 100. With this modified prior,
we run the trial again and Fig. \ref{fig:gaussian-check-bb} shows
the improved BO policy search performance. This relatively simple step can assist
in making the \emph{Policy Prior} useful for tasks irrespective
of their latent factor distribution. However, it comes with a caveat
that the loss of observations could reduce the effect of the prior
if most of the observations are filtered out.

\begin{figure}
\begin{centering}
\subfloat[\label{fig:mean-basketball-9}]{\includegraphics[width=0.2\paperwidth]{imgs/BB/bbv2-11-4-mean-transfer-10env-action5}

}\subfloat[\label{fig:variance-basketball-9}]{\includegraphics[width=0.2\paperwidth]{imgs/BB/bbv2-11-4-variance-transfer-10env-action5}
}\medskip{}
\subfloat[\label{fig:bo-converg-bb-no-filter}]{\includegraphics[width=0.25\paperwidth]{imgs/BB/bb-guassian-filter-siglevel0-standardised-noprior2-BO-action5}

}
\par\end{centering}
\caption{\label{fig:non-gaussian-task}Basketball task's non-Gaussian latent
factor distribution's effect on BO. a) Mean and b) variance of
jump start performances of basketball UPN policies when conditioned
at 10$\times$10 grid of latent values and evaluated across 9 different
ground truth settings (combinations of friction and CoR at 0.2, 0.5,
0.8). c) \emph{Policy prior's} BO convergence performance in Basketball
task without the Gaussianity check,
using only the 5 best actions at each evaluation. All evaluations
are executed in the simulated environment with Basketball validation
test set.}
\end{figure}

\subsection{\label{sec:dqn_upn}DQN Based Universal Policy Network}

For training UPNs for contextual bandit tasks, we use Deep Q-Networks \cite{mnih2015human} as the
underlying agent. When implementing the DQN based UPN, we augment a DQN architecture
adopted from PHYRE \cite{Bakhtin2019-dq} by supporting conditioning
with latent factors in addition to actions (Fig. \ref{fig:dqn-upn}).
For this purpose we use a FiLM layer \cite{perez2018film} due to
its ability to modulate between two signals. We train this model on
simulator generated trajectories by sampling latent factors for a
given task.

\begin{center}
\begin{figure}
\begin{centering}
\includegraphics[width=0.3\paperwidth]{imgs/policy-net-v4}
\par\end{centering}
\caption{\label{fig:dqn-upn} A DQN based UPN design. Latent and action vectors
are encoded using MLP layers, which are used to condition the observations
encoded by a residual network \cite{he2016deep} using FiLM layer
\cite{perez2018film}. $\beta$ and $\gamma$ are parameters learnt
by FiLM to modulate conditioning.}
\end{figure}
\par\end{center}

\subsection{Universal Policy Network Training}

We use the DQN based UPN implementation introduced in Sec. \ref{sec:dqn_upn}
to find policies for our contextual bandit tasks. In these tasks,
we keep the density of objects consistent and maintain friction and
coefficient of restitution (CoR) as the unknown latent factors. When
training the UPN, we select 12 combinations of friction and CoR in
the range of {[}0, 1{]} from a grid with resolution of 0.3 as conditioning
latent points. We then train the UPN on tasks in the training fold
of dataset, each of which is replicated on all 12 latent factor combinations
(i.e., each task is simulated under all sampled latent factor settings).
In order to decide when the UPN has sufficiently converged to an overall
good policy, we evaluate the UPN's policy at 100 sampled latent value
points, which are then transferred to 9 different simulated ground
truth latent values as shown in Fig. \ref{fig:UPN-convergence}. A
sufficiently converged UPN policy should give relatively high rewards
when the ground truth latent values approximately overlap with the
sampled latent values in the UPN. Leveraging this strategy, we selected
UPN models trained for 32 batches of 24,000 and 48,000 updates for
Bowling and Basketball tasks respectively. Furthermore, this verification
establishes that our DQN based UPN can learn latent conditioned policies
successfully.
\begin{center}
\begin{figure}
\begin{centering}
\includegraphics[width=0.4\paperwidth]{imgs/Phyre/UPN-convergence}
\par\end{centering}
\caption{\label{fig:UPN-convergence}A simple strategy to decide whether the
UPN has converged. 100 sampled latent value combinations of friction
and CoR are evaluated on the UPN to select a policy and then transferred
to 9 different environment settings. The title of each subplot refers
to the ground truth friction and CoR value of the environment in which
the policy is being evaluated at. A well trained UPN should provide
policies that yield high rewards when UPN latent values and ground
truth values match.}
\end{figure}
\par\end{center}

\subsection{UPN Training performances}

Fig. \ref{fig:upn-training} shows the training performances of Universal Policy Networks (UPN) on three MuJoCo environments,
each under two settings of two (2D) and five (5D) unknown latent factors. In the 2D setting, mass of a body node and friction of the
tasks are unknown, whereas in the 5D setting, friction, restitution and mass of 3 body parts are unknown.

\begin{figure*}
\centering{}
\subfloat[Hopper-2D]{\includegraphics[width=0.30\paperwidth]{imgs/upn-training/hopper2d-ep-reward.png}

}\subfloat[Hopper-5D]{\includegraphics[width=0.30\paperwidth]{imgs/upn-training/hopper5d-ep-reward.png}

}\medskip{}
\subfloat[Walker-2D]{\includegraphics[width=0.30\paperwidth]{imgs/upn-training/walker2d-ep-reward.png}

}\subfloat[Walker-5D]{\includegraphics[width=0.30\paperwidth]{imgs/upn-training/walker5d-ep-reward.png}

}\medskip{}
\subfloat[Half-Cheetah-2D]{\includegraphics[width=0.30\paperwidth]{imgs/upn-training/cheetah2d-ep-reward.png}
}
\subfloat[Half-Cheetah-5D]{\includegraphics[width=0.30\paperwidth]{imgs/upn-training/cheetah5d-ep-reward.png}
}
\caption{\label{fig:upn-training} UPN average episodic reward during training (in simulation) for three MuJoCo tasks: Hopper, Walker2D and Half-Cheetah.  a, c, e) 2 unknown latent factors (2D) setting (mass and friction), and b, d, f) 5 unknown latent factors (5D) setting (friction, restitution and mass of 3 body parts).}
\end{figure*}

\subsection{Implementation Details}

When conducting BO based policy search, we used GPy\cite{gpy2014} to build the GP model
and Emukit \cite{emukit2019} for Bayesian Optimisation execution.
All \emph{Policy Prior} and \emph{No Prior} results reported are averaged over 5 trials, each
of which is initialised with its random
number generator seed mutually exclusively set to one of 50, 100,
150, 500 and 1000. For all BO operations, we use RBF kernel, Expected
improvement (EI) as the acquisition function, with LBFGS as the optimiser. During BO, to maintain the matrix is well-conditioned, we verify the condition number of the kernel matrix is at most 25,000 and add random noise from a range of {[}0.1, $10^{-5}${]} to the diagonal if it exceeds this threshold.

For no prior baseline, the process starts
with 3 random samples and proceeds to build the GP. In MuJoCo tasks,
BO samples within the bounds {[}0, 1{]}, as all features are normalised.
For bandit tasks, friction range is {[}0, 3{]} while CoR is {[}0,
1{]}.

For MuJoCo tasks, depending on the setting (2D or 5D), we set the ground truth to a vector of length 2 or 5,
generated with \emph{NumPy} random.uniform()\footnote{https://numpy.org/doc/stable/reference/random/generated/numpy.random.uniform.html} with random number generator seed set to 1. To separate the real-world from the simulated environment,
we use a damping value of 3 using \emph{PyDart} simulator's \emph{set\_damping\_coefficient} API call.

Contextual bandit tasks maintain standard three folds of task sets
and a maximum action size to sample from. The Bowling task uses 5000
actions, with 100 tasks in total split to 60 training, 20 validation
and 20 test tasks. The Basketball task utilises
40,000 actions, with a dataset containing 25 tasks, split into 15
training, 5 validation and 5 test sets. When setting up the real-world,
for both tasks a damping of 0.8 is used whereas in the simulated environment
no damping value is used. For the \emph{Estimated} baseline,  6,000 real-world interactions for the Bowling task and 1,500 for the Basketball task have been used to minimise the difference between real-world and simulated trajectories.

\begin{comment}

\begin{figure*}
\centering{}
\subfloat[2Dim-Walker2D]{\includegraphics[width=0.23\paperwidth]{imgs/2D/walker-2d-500win.png}

}\subfloat[]{\includegraphics[width=0.13\paperwidth]{imgs/2D/jumpstarts/transfer-walker-2d-500win.png}
}\subfloat[5Dim-Walker2D]{\includegraphics[width=0.23\paperwidth]{imgs/5D/walker-5d-500win.png}

}\subfloat[]{\includegraphics[width=0.13\paperwidth]{imgs/5D/jumpstart/transfer-walker-5d-500win.png}

}\medskip{}
\subfloat[2Dim-Half-Cheetah]{\includegraphics[width=0.23\paperwidth]{imgs/2D/cheetah-2d-500win.png}
}
\subfloat[]{\includegraphics[width=0.13\paperwidth]{imgs/2D/jumpstarts/transfer-cheetah-2d-500win.png}
}
\subfloat[5Dim-Half-Cheetah]{\includegraphics[width=0.23\paperwidth]{imgs/5D/cheetah-5d-500win}
}
\subfloat[]{\includegraphics[width=0.13\paperwidth]{imgs/5D/jumpstart/transfer-cheetah-5d-500win.png}

}\medskip{}
\subfloat[2Dim-Hopper]{\includegraphics[width=0.23\paperwidth]{imgs/2D/hopper-2d-500win.png}
}\subfloat[]{\includegraphics[width=0.13\paperwidth]{imgs/2D/jumpstarts/transfer-hopper-2d-500win.png}
}
\subfloat[5Dim-Hopper]{\includegraphics[width=0.23\paperwidth]{imgs/5D/hopper-5d-500win.png}
}\subfloat[]{\includegraphics[width=0.13\paperwidth]{imgs/5D/jumpstart/transfer-hopper-5d-500win.png}
}
\caption{\label{fig:gym-2d}Performance in terms of the cumulative reward using a Bayesian Optimisation policy search with \emph{Policy prior} on three MuJoCo tasks and their respective jump start performances,
a-b),e-f),i-j) when there are \textbf{two} unknown latent parameters,
mass and friction in the real world, and c-d),g-h),k-l) when there
are \textbf{five} unknown latent parameters: friction, restitution and mass of 3 body parts
Hopper, Walker2D and Half-Cheetah. For all tasks a 500 step horizon is used and jump start is averaged over 100 random episodes.
All \emph{Policy Prior}, \emph{No prior} and \emph{DR} are trained for $2\times10^{8}$ steps in a simulated environment.}
\end{figure*}

\begin{figure*}
\begin{centering}
\subfloat[Bowling-BO\label{fig:phyre-bo-action5}]{\includegraphics[width=0.20\paperwidth]{imgs/Phyre/prior2/phyre-20a-colour-2.png}

}\qquad{}\subfloat[Bowling-jumpstart\label{fig:phyre-jump-start-action5}]{\includegraphics[clip,width=0.17\paperwidth]{imgs/Phyre/prior2/jump-start-20a-colour-2.png}
}
\subfloat[Basketball-BO\label{fig:gaussian-check-bb}]{\includegraphics[width=0.20\paperwidth]{imgs/BB/bb-guassian-filter-siglevel01-standardised-noprior2-BO-action5-colour-2.png}

}\qquad{}\subfloat[Basketball-jumpstart]{\includegraphics[width=0.17\paperwidth]{imgs/BB/bb-guassian-filter-siglevel01-standardised-noprior2-jump-start-action5-after800trans-colour-2.png}
}
\par\end{centering}
\caption{\label{fig:low-fidelity}\emph{Policy Prior's} policy search and jump
start performances using a low fidelity set for bandit tasks, (a-b) Bowling
task with 20 best actions and (c-d) Basketball task with 5 best actions.
a,c) BO policy search process for \emph{Policy Prior} and \emph{No prior} on
the validation set. b,d) Jump start performance of \emph{Policy Prior}
compared against the baselines on test set.
}
\end{figure*}

\end{comment}

redpajama_set_name: RedPajamaArXiv


Which GPU did you use ?
Will intel UHD 630 graphics run beamng?
What Processor did you use?
hello ecoNadder77 :) i love this game!!!
what happened with the Ford F250??
What BeamNG car would you like in real life?
If you only had $10,000, what car would you buy?


text:
Where do I belong? - Macleans.ca
That mysterious substance guidance counsellors call 'fit' is not so mysterious anymore
by Maclean's
Studying into the night at Mount Saint Vincent | Photograph by Andrew Tolson
Deanna Jarvis, the 19-year-old first-year student on our cover, says she knows the University of Guelph is the right place for her. She's just not sure why. Maybe it's the gold and red leaves that litter the campus in the fall. She could never live in a concrete jungle, she says. Perhaps it's that Guelph offers a rare major (adult development, families and wellbeing) that will teach her how to help people. "I just like to listen to friends and help them," she says. Or maybe it's that Guelph is a big enough school to keep famous playwrights like Judith Thompson on staff. Jarvis, a parttime actor, is a huge Thompson fan. Whatever the reason, Guelph just seems to fit.
Parents, students, university presidents and even education marketers are trying to nail down exactly what makes a school fit. Traditionally, school size and city size were the shorthand for determining where a particular student should go. Big schools offer more cultural opportunities; tiny schools offer more personal interaction, or so the theory goes. Those rules still apply, but sociologist James Côté, of the University of Western Ontario in London, Ont., has found another predictor for what he calls the "goodness of fit." His research found students do best when their inner motivations match what the environment has to offer.
Tom Traves, president of Dalhousie University, agrees that students should look inward to determine the best school for them. "For some students it will be a small, intimate, collegial environment," says Traves. "For other students, their personalities will be sufficiently expansive and their strength of purpose and needs will be such that going to a small environment will be too much like an extension of high school."
Côté would agree, but says university officials are not the only people to ask. "You'll have to do the digging yourself," he says. Some "universities don't want to alienate prospective students who aren't the right fit," he explains. "Because they're funded by tuition and the number of bums in seats."
Assuming they're not going to university because of parental pressure, most students have one of three motivations, according to Côté: the "personal and intellectual" motivation, the "career and materialism" motivation, or the "humanitarian" motivation.
For the student whose goal is to develop personally and intellectually, a small liberalarts oriented school is best, he says. "A good liberal arts education really requires smaller class sizes, so you can have seminars and contact with faculty," he explains. "You'll also be required to do more public speaking and writing. A large school simply can't do this." St. Francis Xavier in Antigonish, N.S., and Quest University in Squamish, B.C., are examples of schools where students seeking personal and intellectual growth will find it, he says.
Large, reputable schools like McGill and the University of Toronto fit students who are personally and intellectually motivated, says Côté, but be sure "you're outgoing or able to work on your own." Students who choose the school primarily for its reputation, says Côté, need to remember that "they may never see any of the profs that make those schools famous."
The second type of student, the "careeristmaterialist," is someone who wants a degree mainly for the job and prestige. "The careeristmaterialist might fit at schools that are vocationally oriented," says Côté. "We're going that direction at Western," he says, giving the example of the increasing popularity of degrees like the bachelor of management and organizational studies over the traditional broad B.A.
The third (and more rare) motivation to study is altruism. Côté offers King's University College (a Western affiliate) as a good fit for the "humanitarianism-motivated" student, because of its social justice focus.
Ken Steele, an education marketing expert, agrees with Côté that universities themselves are unlikely to help you determine fit. Most universities are still trying to be "everything to everyone," he says. However, he has seen a few encouraging examples of schools that are marketing with "goodness of fit" in mind. "Acadia [in Wolfville, N.S.] actually says it's not for everyone," explains Steele. "They want students to know they're coming to a small town and that's going to be a shock for some of them."
William Barker, president of the University of King's College in Halifax (an even smaller school than Acadia), suggests visiting as many schools as possible, sitting in on lectures, and staying overnight with a friend.
That's advice Côté wants parents to hear. He says more parents should encourage their offspring to explore far and wide; too often they encourage offspring to choose the closest school to home in order to save money. "You may save a lot financially in the short run, but you will have lost in the long run," he says. If a person fails at university because it's the wrong fit, they risk losing millions of dollars in lifetime earnings, he explains—and it's not a cheap investment. "If parents were forking out this kind of money in the stock market or real estate, they'd look at it much more carefully," says Côté.
Of course, not everyone can afford to fly around the country to research each school. That's why Maclean's asked successful students from four schools exactly what makes their university the right fit for them. Their answers prove just how important it is for future students to ask themselves who they are and why they want a degree. Why? Just ask Côté. "If you don't develop goals of what you want to get out of university, you potentially squander the most transformative experience of your life."
best students
careerist
University of King's College
university of western ontario
university rankings issue
World ranking names three Canadian universities in reputation survey
World ranking names nine Canadian universities to prestigious list for 2019
My encounters with CSIS
World university ranking puts Canadian school among the top 20
Ranking Canadian Universities by Marijuana Use of Students


text:
require "net/http"
require "uri"
require "tumasms/version"
require "tumasms/connection"
require "tumasms/message"

module Tumasms
end

redpajama_set_name: RedPajamaGithub


text:
Q: Expression/word for talking about a seemingly different subject for a while? Let's say you give a presentation where you intent to talk about A, B and C. But before you get to talk about B you feel that since it's a subject in a field not everyone in the audience might be familiar with (or remember well) you have to give a short introduction/overview into the field first before really talking about the subject. In some cases it might even seem unexpected to the audience members which is why you might want to explicitly preface this part of your presentation.
Now how would you call this kind of interruption?

*

*A digression?

*A tangent?

*An excursus? (Which is what you might call it in German.)

*…

(While a single word would be nice a phrase would work as well.)

A: More possibilities in the spirit of introduction and preface:
background    "Let me provide some background."

groundwork   "Let's start with some groundwork."

lemma      "Lemma A will be used in the proof
                             of Theorem T." (mathematics)

A: Perhaps you might call that part of the speech an 'interlude', where 'interlude' is defined as

*transf.
  a. An interval in the course of some action or event; an intervening time or space of a different character or sort.

["interlude, n.". OED Online. December 2015. Oxford University Press. http://www.oed.com/view/Entry/97950?rskey=jSOdPE&result=1&isAdvanced=false (accessed January 02, 2016).]
Or you could use 'interlude' with a descriptive adjective, whichever seems most appropriate to you. Here are some possibilities:

*

*rhetorical interlude

*transitional interlude

*introductory interlude

*explanatory interlude

redpajama_set_name: RedPajamaStackExchange


text:
Ilford Recorder > News
'Free' school a boost for Redbridge pupils?
Published: 12:09 PM September 21, 2010
A 'FREE' school could provide a boost for education in Redbridge according to one teacher, who is inviting views on setting up one of the new estabishments
Mark Gittens, a science teacher at Wanstead High School, Redbridge Lane West, Wanstead, wants to open up a debate on whether a free school - a concept devised by the coalition government - would be welcomed in the borough.
The 51-year-old, who served as a Labour councillor for a matter of days in May before it was found his job as a teacher made him ineligible to stand, thinks bringing one to Chadwell Heath would "boost" the borough's education landscape.
The government's free schools gives groups including teachers, parents, businesses and charities the power to establish their own school in response to demand.
Like academies, they would run free of local authority control.
Mr Gittens, of Flora Gardens, Chadwell Heath, said: "I think there's a need for a free school, probably a secondary, in the borough, but I would like to hear the views of other people.
"If people agree there's a need I'd be interested in taking it further."
Allaying fears the school would be run by inexperienced people, he said: "It wouldn't be for parents rebelling. Anyone involved would have to meet government criteria and go through all the rigorous procedures.
"The people involved will know the education system inside out and that would be its great strength."
Groups looking to set up free schools can get support and advice from the independent charity the New Schools Network.
Government ministers are pledging to cut the red tape which covers the formation of new schools, including planning laws and other rules.
Sixteen free schools will be set up across the country over the next year.


text:
Vingle Lets You Mingle With People Who Share Your Interests
Catherine Shu	@catherineshu / 6 years
Vingle is a fun, community-oriented site from two of the three founders of Viki, the crowdsourced subtitling platform that was acquired by Rakuten for a reported $200 million last year.
Changseong Ho and Jiwon Moon saw how devoted fans on Viki were to translating and subtitling the dialogue in their favorite movies and TV series, and wanted to create a network were people could participate in interest-based groups about things they are equally passionate about.
Vingle is based on Korea, but with the launch of its iOS app, it hopes to find more users in different countries (the site already has a large cohort of English-language participants). It's also available on Android.
On Vingle, fans can join groups focused on things ranging from men's and women's fashion to trending topics like the Tour de France. The site currently has 2.3 million visitors and in June it saw 100 million page views.
"If you look at the history of our founders and Viki, they were really fascinated even before Viki to find communities of subtitlers. The question raised was why are these people doing this subtitling and translation for free? They are driven by being in a community they love. The founding vision of Vingle is to share with likeminded people about everything, not just videos," says Mark Tetto, Vingle's chief strategy officer.
In Korea, Vingle competes with Naver Cafes, which is a community platform by the maker of messaging app Line. Vingle is also similar to Reddit, but it takes a much more visually-oriented approach.
There are also commonalities between Vingle and Pinterest, because both are heavy on content such as fashion, DIY, and entertainment and have a similar mosaic-style layout.
To set itself apart from Pinterest, Vingle is redesigning its website. "We are in a stage right now where we're defining who we are as a community platform," says Tetto. "People see the UI and it feels to them like Pinterest, but that will change very much."
"More generally, it's about what is a community versus just interests," he adds. "We provide spaces people can join. There are over 3,000 communities and they are very tailored to your particular interests. One of the hardest things to do is to create a massive audience, whether you are on Pinterest, a blogger, or on Facebook."
Part of Vingle's appeal to users is an instant audience for their contributions in groups with memberships of thousands of other people. Though Vingle is still hashing out a monetization strategy, the same thing may also appeal to potential advertisers because they can quickly see what other interests specific groups tend to gravitate toward.
"The structure of the service is really different than other social networks because if someone likes a product, like Chanel, you can find what else they love, what they like to eat, or what kind of cars they like. So right now our content system has that potential," says Sanghoon Kim, the site's director of marketing and communications, adding that Vingle may eventually monetize by showing sponsored content relevant to each group.


Textures only for the default B737. Repaint By Robert Hawk.
Screenshot of America West Boeing 737 in flight.
Done in America West's Livery, a replica of what I flew in from KSLC, UTAH to KPHX, Arizona (Flight 221) dedicated to the great crew of that flight who made it a pleasurable flight.
The archive amw_737.zip has 15 files and directories contained within it.


Showing 6 to 9 of 9 events.
Come in for a first look at our new seasonal colours, textures, fabrics and pieces. Enjoy a glass of fizz, a bite to eat and enter our exciting prize draw competition!
An exhibition of paintings by members of the Canterbury Art Society and photographs by Andy Goodenough. The exhibition will be open every Saturday until Saturday, 13th April.


text:
Q: Amplify for Govcloud? Does AWS offer an authentication for Cognito that is compatible with react and gov cloud that is not Amplify. I am using Govcloud and my region is not in amplify(amplify does not support govcloud). How do I authenticate with OIDC and cognito via govcloud without amplify?

A: I'll need some more details about your use case with OpenID Connect, but as far as authenticating with Cognito in a Gov Cloud region, use this library: https://www.npmjs.com/package/amazon-cognito-identity-js. It actually resides in the Amplify JS repository and is actively maintained.
Here is a basic example of logging in with a username and password.
const userPool = new AmazonCognitoIdentity.CognitoUserPool({
  UserPoolId: '...', // your user pool id here
  ClientId: '...'    // your client id here
})

function login(username, password) {
  const authenticationDetails = new AmazonCognitoIdentity.AuthenticationDetails({
    Username: username,
    Password: password,
  })

  const cognitoUser = new AmazonCognitoIdentity.CognitoUser({
    Username: username,
    Pool: userPool,
  });

  cognitoUser.authenticateUser(authenticationDetails, {
    onSuccess: function(result) {
        const idToken = result.getIdToken().getJwtToken();
        console.log(idToken);
    },
    onFailure: function(err) {
        console.log(err);
    },
  });
}

Also note, that when you add an app client to your User Pool, the Generate client secret option must be unchecked when creating a web app; the Amazon Cognito Identity SDK for JavaScript doesn't support apps that have a client secret simply because the client secret could be easily viewed in your code.


Citrus, herb infused grilled seitan sticks.
Crispy baked tempeh buffalo wings tossed in hot sauce and served with creamy ranch.
Blue corn chips piled high with crisp romaine lettuce, black beans, queso, spicy jalepeno sour cream, and garden fresh pico. Topped with a dollop of chipotle tofu sour cream.
Golden baked whole wheat tortilla filled with daiya cheddar cheese, smashed beans and grilled vegetables. Served with fresh pico and tofu sour cream.
Spicy tempeh buffalo wings over crisp romaine lettuce and spring mix. Topped with cabbage, carrot sticks and crunchy celery. Served with a creamy ranch dressing.
Crisp romaine lettuce, sun-dried tomatoes, olives, and croutons tossed in our classic lemon Caesar dressing. Topped off with walnut sprinkle cheese.
Spiced-seitan served over brown rice and black beans. Topped with guacamole, caramelized onions, roasted red peppers, crisp romaine lettuce and finished with a dollop of jalapeno aioli.
Coconut crusted Tempeh served with brown rice and seasonal vegetable mix. Topped with creamy Thai coconut sauce and pineapple mango chutney.
Creamy home-style mac and cheese topped with coconut bacon and sriracha aioli.
Seitan kabobs served over brown rice and seasonal vegetable mix. Topped with our spicy peanut sauce.
Our spicy buffalo wings in a whole wheat wrap with lettuce, cabbage, carrots sticks and ranch dressing.
Spicy shredded seitan smothered in our homemade bbq sauce. Piled high on a toasted sesame roll with caramelized onions, pickled cucumber salad, fresh arugula and sriracha aioli.
Smokey applewood tempeh, sautéed apple, caramelized onions, and fresh arugula served on a toasted semolina roll. Drizzled with a sweet and tangy maple mustard sauce.
Grilled portobello mushroom, roasted red peppers, caramelized onions, artichoke hearts, basil marinated dark greens, and garden aioli on a rustic roll. Finished with a balsamic reduction.
A Vietnamese style sandwich with shredded seitan, pickled carrots, minted-cucumbers, diakon, fresh cilantro, sprouts, and a spicy jalapeno aioli on a rustic roll.
An old favorite goes green. Smokey Tempeh strips, crisp lettuce, garden tomatoes, and a smear of vegan mayonnaise served on toasted whole wheat bread.
A classic sandwich with all the fixins. Marinated baked Tempeh layered with sauerkraut, homemade cheese, thousand island dressing and spicy brown mustard. Served on rye bread.
Tortilla filled with marinated and baked Tempeh, crisp lettuce, cabbage and carrots. Smothered in our own homemade barbeque sauce and smoky chipotle aioli.
Tortilla filled with Cajun spiced Seitan, sautéed dark leafy greens, caramelized onions, crisp carrots, cabbage and sweet mustard sauce.
A Red Bank classic! Our homemade Tofu, sunflower seed and brown rice patty baked until golden. Served on a whole grain bun with vegan mayonnaise, romaine lettuce, sliced tomato and red onion.
For children 12 and under only please or an additional charge will be added.
Fresh apple and lemon with a pinch of ginger over ice.
A fun blend of celery, apples, kale, cucumber, lemon, and parsley.
Fresh juiced lemons with maple syrup and a dash of cayene pepper. This spicy drink is served over ice.
carrot, ginger, garlic, lemon, greens, beet, apple.
2oz shot of ginger, cayenne, lemon, flax oil.
Spirulina, banana, flax oil, blueberries, hemp seeds, and apple juice.
Pineapple and mango blended with a mix of coconut milk and soymilk. Topped with coconut flakes.
Strawberries, apple juice, banana, granola, and cinnamon.
Bananas, chocolate syrup, peanut butter, and soymilk.
Miglior ristorante vegetariano ovunque! Questo ristorante ha un menu molto ampio e assolutamente tutto è delizioso. Ha una grande atmosfera e buon servizio. Il Buffalo bastoni antipasto è incredibile e la pizza di roccia è eccellente.
Mi piace andare a buon Karma Cafe. Luogo accogliente con un sacco di scelta nel menu da scegliere. Il mio preferito è hamburger vegetariani e le insalate. Il personale è cordiale e il prezzo è molto ragionevole.
Buon Karma. Il cibo è eccellente per la salute.
Adoro il cibo qui, tornati il giorno successivo!
Ricevi risposte dal personale della struttura Good Karma Cafe e dai visitatori precedenti.


This paper presents a vision/LiDAR integrated navigation system that provides accurate relative navigation performance on a general ground surface, in GNSS-denied environments. The considered ground surface during flight is approximated as a piecewise continuous model, with flat and slope surface profiles. In its implementation, the presented system consists of a strapdown IMU, and an aided sensor block, consisting of a vision sensor and a LiDAR on a stabilized gimbal platform. Thus, two-dimensional optical flow vectors from the vision sensor, and range information from LiDAR to ground are used to overcome the performance limit of the tactical grade inertial navigation solution without GNSS signal. In filter realization, the INS error model is employed, with measurement vectors containing two-dimensional velocity errors, and one differenced altitude in the navigation frame. In computing the altitude difference, the ground slope angle is estimated in a novel way, through two bisectional LiDAR signals, with a practical assumption representing a general ground profile. Finally, the overall integrated system is implemented, based on the extended Kalman filter framework, and the performance is demonstrated through a simulation study, with an aircraft flight trajectory scenario.
C. Becker, J. Salas, K. Tokusei, and J. C. Latombe, "Reliable Navigation Using Landmarks", Proceedings of IEEE International Conference on Robotics and Automation, Nagoya, Aichi, 1995, Vol.1, pp. 401-406.
C. S. Sharp, O. Shakernia and S.S. Sastry, "A Vision System for Landing an Unmanned Aerial Vehicle", Proceedings of IEEE International Conference on Robotics and Automation, 2001, Vol.2, pp. 1720-1727.
L. S. Coelho and M.F.M. Campos, "Pose Estimation of Autonomous Dirigibles Using Artificial Landmarks", Proceedings of IEEE International Conference on Robotics and Automation, Campinas, Brazil, 1999, pp. 161-170.
H. Durrant-Whyte and T. Bailey, "Simultaneous Localization and Mapping: Part I", IEEE Robotics & Automation Magazine, 2006, Vol.13, Issue2, pp. 99-110.
S. Ahrens, D. Levine, G. Andrews and J.P. How, "Vision-based guidance and control of a hovering vehicle in unknown, GPS-denied environments", Proceedings of IEEE International Conference on Robotics and Automation, Kobe, Japan, 2009, pp. 2643-2648.
S. Yun, B. Lee, Y. J. Lee and S. Sung "Real-Time Performance Test of an Vision-based Inertial SLAM", Proceedings of International Conference on Control, Automation and Systems, 2010, Gyeonggi-do, Korea, pp. 2423-2426.
T. Cornall and G. Egan, "Optic flow methods applied to unmanned air vehicles", Academic Research Forum, Dept. Elect. And Computer Systems Engineering, Monash University, Feb, 2003.
W. Ding, J. Wang, S. Han, A. Almagbile, M. A. Garratt, A. Lambert, and J. J. Wang, "Adding Optical Flow into the GPS/INS Integration for UAV navigation", International Global Navigation Satellite Society Symposium, 2009, Surfers Paradise, Qld, Australia.
S. Zingg, D. Scaramuzza, S. Weiss, and R. Siegwart, "MAV Navigation through Indoor Corridors Using Optical Flow", Proceedings of IEEE International Conference on Robotics and Automation, Anchorage, AK, 2010, pp. 3361-3368.
S. Kohlbrecher, J. Meyer, O. von Stryk, and U. Klingauf, "A Flexible and Scalable SLAM System with Full 3D Motion Estimation", IEEE International Symposium on Safety, Security, and Rescue Robotics, 2011, Kyoto, Japan, pp. 155-160.
L. Huang and M. Barth, "Tightly-Coupled LIDAR and Computer Vision Integration for Vehicle Detection", IEEE Intelligent Vehicles Symposium, Xi'an, China, 2009, pp. 604-609.
P. Moghadam, W. S. Wijesoma, and J. F. Dong, "Improving Path Planning and Mapping Based on Stereo Vision and Lidar", Proceedings of International Conference on Control, Automation, Robotics and Vision, Hanoi, Vietnam, 2008, pp. 384-389.
W. Ding, Optimal Integration of GPS with Inertial Sensors: Modelling and Implementation, Ph.D. thesis, University of New South Wales, Sydney, 2008.
Ribeiro, M., "Kalman and extended Kalman filters: concept, derivation and properties", Technical Report, Institute for Systems and Robotics-Instituto Superior Tecnico, Lisbon, 2004.


Indian Spectator is an independent digital media venture for news pertaining to politics, sports, and technology. We are here to take voices and unbiased opinions of our audience and present it to shape a better India. Started 2 years ago, completed 1.8 lacs news reads in just 4 months.


This outstanding example of a 2018 Ford EcoSport SE FWD is offered by Pugmire Ford Llc. Want more room? Want more style? This Ford EcoSport is the vehicle for you. You can finally stop searching... You've found the one you've been looking for. The Ford EcoSport SE FWD will provide you with everything you have always wanted in a car -- Quality, Reliability, and Character. This is about the time when you're saying it is too good to be true, and let us be the one's to tell you, it is absolutely true. Contact us at 770-832-2457 with any questions. We're always happy to help!
The Ford EcoSport comes with two engine choices. The base car has a 1.0L in-line, 3-cylinder turbocharged engine producing 123 horsepower. A bigger, 2.0L engine with 4-cylinders meanwhile, produces 166 horsepower; though owners will have to pay a small mileage penalty for the extra power. Power is sent to the front tires, or to all-four wheels via a 6-speed automatic transmission. While Ford has not released mileage estimates for the EcoSport, we expect exceptional mileage for an SUV.
The EcoSport comes in four distinct trim levels, starting with the base S version. Standard equipment on the base model include one-touch power windows, automatic headlights, an MP3 capable stereo with six speakers, two USB ports, air conditioning, hill start assist, a rearview camera and an anti-theft system. Outside, the EcoSport has 16-inch wheels, daytime running lights, mirrors with an integrated blind spot system and the ability to tow up to 2000 lbs.
Stepping up to the SE gets buyers Ford's SYNC 3 operating system, SiriusXM Satellite radio, a power moonroof, automatic temperature control, body colored door handles, a trip computer and sensors in the rear bumper to help while backing up.
Titanium edition EcoSports get a much nicer stereo, a voice activated touch screen navigation system, Perforated leather seating surfaces, rain sensing wipers, a leather steering wheel and shift knob and an auto-dimming rearview mirror. Outside, the Titanium is easily recognizable by its fog lamps, 17-inch wheels and additional chrome work. Several safety features are also included on the Titanium, including a cross-traffic alert system and an emergency brake assist system.
The top of the line SES is a little sportier than other EcoSports, starting with the engine. The bigger 2.0L comes standard as does sport tuned suspension. Otherwise the SES is equipped similarly to the Titanium and SE versions of the vehicle.
The Ford EcoSport is a brand new vehicle, slotting in below the escape at the bottom of Ford's SUV lineup. While the escape is the rough SUV equivalent to a compact car, the new EcoSport fills in the open niche of subcompact SUV. It's an interesting vehicle class that has not been experimented with very much in the United States, but Ford is confident that buyers will find plenty to like about the new diminutive little SUV.
Ford have done their homework with the new EcoSport, providing lots and lots of standard features that drivers want at this price point. In addition, the EcoSport promises to be pretty inexpensive to operate and own, especially with its economical 3-cylinder engine. City dwellers who fancy the occasional trip to the countryside will love the EcoSports maneuverability and the way its tiny body is able to swallow up seemingly endless amounts of luggage. The EcoSport also offers all-wheel drive, which is quite a rarity for a vehicle of this size. Prices start at under $20,000.


text:
Moderna seeks approval for COVID-19 vaccine in young children
&nbspThursday, March 24, 2022
Moderna announced today it seeks emergency use authorization from the Food and Drug Administration (FDA) of a two-dose primary series of its COVID-19 vaccine for children ages 6 months to 6 years.
Omicron could fade in 'weeks,' former FDA head tells travel industry
The wave of omicron cases in the U.S. that has disrupted everything from flights to cruises over the past month is likely to fade fast in the coming weeks.
New tourism route to launch between the Dominican Republic, Costa Rica and Panama
&nbspTuesday, January 11, 2022
The Dominican Republic, Costa Rica and Panama are due to launch a brand new tourism route that will make it easier for American travellers to visit all three highly popular destinations.
Top US health body issues 'Level One' travel notice for India
&nbspTuesday, November 16, 2021
Centers for Disease Control and Prevention (CDC) has issued a 'Level One' COVID notice for its citizens travelling to India, saying the risk of contracting the infection and developing severe symptoms may be lower for fully vaccinated.
New US travel rules exclude recipients of two popular vaccines
&nbspThursday, October 28, 2021
Starting Nov. 8 non-citizens traveling to the US must show proof of vaccination for COVID-19, as well as a negative test taken within three days before departure.


At DumpsterWay, we strive to bring you the best service at the best price available. We have a wide selection of dumpster sizes in stock in Marietta, OH, with flexible & timley pickup and delivery. Give us a call today for all your dumpster rental and roll off needs.
Your roll-off dumpster in Marietta, OH is only a phone call away. Don't delay!
Maintain home as neat and clean as you can with the use of any construction Garbage Dumpster for Rent in Marietta, OH. Your house can look the best perhaps during the structure process by consistently placing all got rid of items to the dumpster. Next day Garbage Dumpster for Rent in Marietta, OH is accessible for a easy and cleanse home construction process. Quick elimination of the full dumpster is usually available. Ones construction process can be made simpler with a dumpster.
When you own your personal business you need to think of stuff that many people really don't. While an inferior business might get by by incorporating trash ales, if you own a larger business or just the one that goes through a lot of garbage being a restaurant you very well may have to purchase something slightly larger.
The next task is time period of the actual rental, be accurate in revealing drop along with pick up days of the dumpster, because Garbage Dumpster for Rent in Marietta, OH service providers charge the fee depending on the time period of the actual rental.
Dumpster rental could be the answer if you have a lot of trash. You might have a huge occasion in the planning and need ways to dispose of every one of the trash. You might be planning a big demolition and desire a quick place to store dirt without taking plenty of trips for the dump. Here are a few hints that can help you get commenced.
The important thing to recollect about dumpster accommodations is to do your research. You don't want to find yourself paying for something you don't need and ultimately won't use.
Before picking out which dumpster company to rent by, make sure you understand how many days as well as weeks you may keep the dumpster your parent's house. You want to make sure you have enough time for the job.
Finally, when you're finished while using rented dumpster, an appointment to the rental company for a pick-up is required. All organizations have distinct rental periods, along with knowing the number of days allowed to get a rental is imperative to know in order to making a payment.
It happens to the best of home owners these days. Daily life gets chaotic and stuff starts to gather. Before you know it, ones small storage area areas have been taken over because of the remnants involving cast-off possessions. Consequently stop making excuses regarding the clutter in your life and start setting up a difference using one simple call to your home team junk transporting service. Perfect for cleaning out single-family garage areas or basements, today's residential-sized dumpsters are the answer to your household trash and rubbish eradication needs, which include: Increasing environmental concerns has raised the volume of industries while using Roll Away rental services developed a misconception in which Dumpster services are totally tied to industrial objective. I completely disagree with the statement. Let me tell you that Dumpster services in recent times will be extending their wings to various commercial along with household operates as well. The types of waste given by the the majority of professional Dumpster rental agencies is classified straight into four categories general waste materials, construction waste materials, green lawn waste along with recyclables.
Dumpsters are available in different sizes and choosing the right choice is important considering that the bigger dumpster you end up picking, the more it'll cost you. If you are conducting a house improving or residence expansion task, then you will suffer from a lot of waste materials, which will call for a dumpster size of 30-40 cubic foot. Choosing the right dumpster dimensions not only decides the cost of these types of, but it also decides how efficiently you can move the waste out of your property.
Everyone has noticed dumpsters at corporations and structure sites. Smaller ones are usually behind every commercial as well as restaurant as well as mall place across the country. You will find larger dumpsters that happen to be used on structure sites. Suppose you need a dumpster to your renovation project, large or small? Wherever would you move to locate the right choice? These companies go ahead and take data you have given these individuals, by entering that on a basic form, and locate the best deal and location of a perfect dumpster for your needs. The company you pick will drop off your dumpster, then understand it again when it's complete. This company will in most cases be a regional one so it's cheaper as well as quicker in comparison with getting a rotate off Garbage Dumpster for Rent in Marietta coming from some remote area many miles out, or out of state. As you get your current quote they're going to no doubt wish to know if you might have all garbage, some recyclables, and just what types if you have the latter. It is usually better to delete materials when you can actually.
These rotate off dumpsters are probably the most popular dimensions used amidst home owners. They're great for doing away with excess garbage after handling some common maintenance on the house, specially when redecorating or maybe cleaning out place in the loft.
Once the residence has been cleaned of the tenant's issues, it's time to concentrate on repairs. Evictees are often terrible property owners (they had to end up being evicted, after all!), and they are able to do some very extensive deterioration to your property that needs to be repaired one which just rent it out again. Odds are that you will have to replace your carpeting from the rental; few renter's leave whole carpeting. If your former actuel had domestic pets, it is essential to substitute the carpet to take out all contaminants. However, in the event that there has been any kind of damage carried out to the surfaces or repainting is necessary, time savings and trouble by keeping all those old floor coverings installed to be drop cloths while you restoration and paint. Once the fixes are done, disparaging offer out as well as replace the previous carpet. Carry out any essential maintenance about appliances as well as fixtures, and start showing the exact property to be hired again. Far better luck along with your next property owners! A good dumpster support will not keep you waiting a long time. They will get yourself started on any kind of project on the same day an individual contact them if you appreciate. All you have to accomplish is create that telephone call.
Maybe you are renovating your child's room now that they're a little older and desires a more arranged bedroom. You may want to add built-in shelves as well as a built in computer desk or maybe entire wall surface area. New carpeting or wooden floors may be on the agenda at the same time. You will have to use a contractor to disparaging offer out the aged carpeting as well as install the new carpeting. You will want to rent a dumpster to get rid of all the aged carpeting as well as debris.
A number of services that we buy that seem so run of the mill it doesn't matter whom we use. We physique that no matter who we use, the result is going to be pretty much the identical. The ones that promptly come to thoughts are such things as taxi cabs, flower delivery, plumbers, and so forth. If you are doing any kind of makeovers or key yard function, you might think garbage bin rentals would likely fall into of which category far too. Really even though, with the services you will get drastically spun sentences depending on which team you hire.
You additionally need to keep in mind that Garbage Dumpster for Rent in Marietta, OHs possess weight restrictions on them. Every single dumpster size will have unique excess weight restrictions. Before selecting your rental, it is vital to consider the excess weight restrictions about the dumpsters that you are considering. Some of the throw off pots may have excess weight restrictions associated with 10 tons. Several of the smaller front side load dumpsters possess far more compact weight restrictions, so it's essential to find out about these kinds of restrictions at the start.
Be it a family, corporate or perhaps a private firm, each of these groups can make using such solutions. These services may help you get rid from the trash in a green and convenient approach.
Alexandria, VA is undoubtedly an upscale community. Those that dwell there hold their homes and yards shopping beautiful. If you intend on going from your large home in to a condo or even nursing home, you might need a Garbage Dumpster for Rent in Marietta, OH. After residing in the same property for over 25 to fifty in addition years you might have accumulated an abundance of stuff. Almost certainly your children and those that are close up to you have the items so long as wanted. You might have had a rummage purchase or an auction on the majority of your objects. You still have an abundance of stuff remaining that not also a second hand retail outlet would want. (It's possible some of it ended up being damaged or just is no longer useful.) This is where Garbage Dumpster for Rent in Marietta, OH throughout Alexandria, VA can be purchased in.
Perhaps you are creating a party or even some form of accumulating or even doing spring cleaning in your home you would possibly want to make use of disposal receptacles. With a removal bin, you may leave your garbage bin in numerous areas where family and friends can place their trash for straightforward cleanup afterwards.
Find a reputable dumpster service exactly who offers dumpsters intended for rent for cleaning upward overflowing garages, storage suites, attics or even basements or for cleaning up properties.
Before you a single thing, you need to go into the property and evaluate the scenario. Did your tenant depart personal property behind? Junk? What sort of damage did your tenant do to the property alone? Will it be essential to replace your carpets, patch holes inside the wall, or even paint? Execute a detailed examination of the property to find out what exactly will have to be done to to allow new renters to live generally there in the future.
Probably the most important facts you need to look at when it comes to dumpster renting is that dimension is very important. You'll see that dumpsters come in a number of sizes and you will probably want to pick the appropriate sizing for your needs. When you need to make sure that it is big enough to hold all the trash you should have until pick up, you don't want to buy something that is just too large for your requirements either. Look at the dimensions of your dumpster before choosing. Furthermore, consider the dissimilarities between front side load and roll down containers an internet to choose the right rental to your waste administration needs.
The weather conditions forecast might be calling for rainfall or snowfall and you want to get the top job done over the following few days. The good news is you can get your own supplies quickly such as the asphalt shingles for the roofing and a dumpster the very next day.