text:
\section*{Abstract}

This paper presents the Homeo-Heterostatic Value Gradients (HHVG) algorithm as a formal account on the constructive interplay between boredom and curiosity which gives rise to effective exploration and superior forward model learning. We envisaged actions as instrumental in agent's own epistemic disclosure. This motivated two central algorithmic ingredients: devaluation and devaluation progress, both underpin agent's cognition concerning intrinsically generated rewards. The two serve as an instantiation of homeostatic and heterostatic intrinsic motivation. A key insight from our algorithm is that the two seemingly opposite motivations can be reconciled---without which exploration and information-gathering cannot be effectively carried out. We supported this claim with empirical evidence, showing that boredom-enabled agents consistently outperformed other curious or explorative agent variants in model building benchmarks based on self-assisted experience accumulation.

\section{Introduction}

In this study, we argue that action is instrumental in epistemic disclosure in and of agent itself. The implication of this statement is twofold: (1) for agents whose innate goal appeals to their own knowledge gain, the occurrence of curiosity rests upon the presence of devaluation (and hence goal-directedness); (2) boredom---consequential to devaluation---and curiosity entail a mutually reinforcing cycle for such kind of disclosure to ensue.

Animal studies have shown that learning stimulus-response (S-R) association through action-outcome reinforcement is but one facet of instrumental behaviour. Internally, animals may build models that assign values to reappraise experienced outcomes. This expands the landscape of instrumental behaviour to include stimulus-outcome-response (S-O-R) learning system---or goal-directed learning \citep{Balleine.1998}. Goal-directed behaviour is known in both empirical and computational approaches to support adaptive and optimal action selection \citep{adams1981instrumental, adams1982variations, mannella2016goal}. Central to such behavioural adaptiveness is devaluation. This means for a given action-outcome pair the associated reinforcing signal is no longer monotonic. Instead, outcome value varies under reappraisal within according to their relevance to or attainment of goal.

One classic paradigm of devaluation that shapes agent's behavioural pattern is of food via satiation. In the context of epistemic disclosure, an analogy can be drawn between devaluation and the emergence of boredom, in which one's assimilation of knowledge reduces the value of similar knowledge in future encounter. The relationship between boredom and outcome devaluation has a long history in psychological research. Empirical findings indicated that boredom is reportedly accompanied by negative affective experience, suggesting that experienced outcomes are intrinsically evaluated and considered as less valuable \cite{bench2013function, van2012boredom, fahlman2009does, perkins1985cognitive, vodanovich1991boredom}. Psychophysiological studies also demonstrated that boredom plays an active role of information-seeking behaviour. Subjects showing higher levels of reported boredom are accompanied by increased autonomic arousal, such as heart rate and galvanic skin response. These findings is in line with our key notion that boredom intrinsically and actively drives agents learning behaviours \citep{london1972increase, harris2000correlates}. Consistent with our argument, evidence also showed that boredom is associated with increase in creativity \cite{harris2000correlates, schubert1977boredom, schubert1978creativity}. This suggests that the presence of boredom serves to reconfigure agent's instrumental device in order to escape devalued states.

Curiosity, irrespective of being a by-product of external goal-attainment or an implicit goal in and of agent itself, is often ascribed to as a correlate of information-seeking behaviour \citep{2013gottlieb}. Behaviours exhibiting curious quality are observed in humans and animals alike, suggesting an universal role of curiosity in shaping one's fitness. Though the exact neural mechanism underlying the emergence of curious behaviour still remains obscure, current paradigms have their focus on (1) novelty disclosure and (2) uncertainty reduction aspects of information-seeking \citep{pathak2017icm, friston2017curiosity, bellemare2016count, ostrovski2017count}. Indeed, both aspects can be argued to improve agent's fitness in epistemic landscape if the agent elects to incorporate the novelty or uncertainty.

In intrinsic motivation literature \citep{intrinsicmotiv}, although one can readily associate boredom with homeostatic motivation and curiosity with heterostatic motivation, our argument suggests they can in fact be complementary. Our contribution thus pertains to the reconciliation of homeo-heterstatic motivations.

\section{Markov Decision Process}

In what follows, we briefly review preliminaries for the ensuing algorithm. We focus on well-established themes surrounding typical reinforcement learning, including Markov Decision Process and value gradients as a policy optimisation technique.

In Markov Decision Process (MDP) one considers the tuple $(S, A, R, P, \pi, \gamma)$. $S$ and $A$ are spaces of real vectors whose member, $\boldsymbol{s} \in S$ and $\boldsymbol{a} \in A$, represent states (or sensor values) and actions. $R$ is some reward function defining the mapping $R: S \times A \to \mathbb{R}$. The probabilities associated with states and actions are given by the forward model $P (S^\prime|S=\boldsymbol{a}, A=\boldsymbol{s})$ and the action policy $\pi(A|S=\boldsymbol{s})$. Throughout the paper we use the `prime' notation, e.g., $\boldsymbol{s}^\prime$, to represent one time step into the future: $\boldsymbol{s}^\prime=\boldsymbol{s}(t+1)$.

The goal of MDP is to optimally determine the action policy $\pi^\ast$ such that the expected cumulative reward over a finite (or infinite) horizon is maximised. Considering a finite horizon problem with discrete time, $t \in [0, T]$, this is equivalent to $\pi^\ast = \arg\max_\pi \mathbb E_{\boldsymbol{a} \sim \pi}\left[ \sum_{t=0}^{T}\gamma^t R(\boldsymbol{s}(t), \boldsymbol{a}(t)) \right]$, where $\gamma \in [0, 1]$ is the discount factor.

Many practical approaches for solving MDP often resort to approximating state-action value $q(\boldsymbol{a}, \boldsymbol{s})$ or state value $v(\boldsymbol{s})$ functions \citep{sutton1998reinforcement, deepmind2013atari, deepmind2015lillicrap, svg}. These value functions are given in the Bellman equation
	\begin{equation}
	\begin{aligned} \label{eq:bellman}
	v(\boldsymbol{s})
	&=
	\mathbb{E}_{\pi(\boldsymbol{a}|\boldsymbol{s})}
	\Bigl[ R(\boldsymbol{a}, \boldsymbol{s}) + \gamma q(\boldsymbol{a}, \boldsymbol{s}) \Bigr] \\
	&=
	\mathbb{E}_{\pi(\boldsymbol{a}|\boldsymbol{s})}
	\Bigl[ R(\boldsymbol{a}, \boldsymbol{s}) + \gamma \mathbb{E}_{P(\boldsymbol{s}^\prime|\boldsymbol{a}, \boldsymbol{s})} [v(\boldsymbol{s}^\prime)] \Bigr]
	\end{aligned}
	\end{equation}
When differentiable forward model and reward function are both available, policy gradients can be analytically estimated using value gradients \citep{2012fairbank, svg}.

\section{Homeo-Heterostatic Value Gradients} \label{sec:algorithm}

This section describes formally the algorithmic structure and components of the Homeo-Heterostatic Value Gradients, or HHVG. The naming of HHVG suggests its connections with homeostatic and heterostatic intrinsic motivations \citep{intrinsicmotiv}. A homeostatic motivation encourages an organism to occupy a set of predictable, unsurprising states. Whereas, a heterostatic motivation does the opposite; curiosity belongs to this category.

The algorithm offers reconciliation between the two seemingly opposite qualities and concludes with their cooperative nature. Specifically, the knowledge an organism maintains about its homeostatic boundary helps instigate outbound heterostatic drives. In return, satisfying heterostatic drives broadens the organism's boundary of comfort. As a consequence, the organism not only improves its fitness in terms of homeostatic outreach but also becomes effectively curious.

It is instructive to overview the nomenclature of the algorithm. We consistently associate homeostatic motivation with the emergence of {\it boredom}, which reflects the result of having incorporated novel information into one's knowledge, thereby diminishing the novelty to begin with. This is conceptually compatible with outcome {\it devaluation} or induced satiety in instrumental learning. {\it Devaluation progress} is therefore referred to as one's epistemic achievement. That is, the transitioning of a priori knowledge to one of having assimilated otherwise unknown information. The devaluation progress is interpreted as an instantiation of intrinsic reward. The drive to maintain steady rewards conforms to a heterostatic motivation.

An intuitive understanding of HHVG is visualised in Figure \ref{fig:intuition}. Imagine the interplay between a thrower and their counterpart --- a catcher. The catcher anticipates where the thrower is aiming and makes progress by improving its prediction. The thrower, on the other hand, keeps the catcher engaged by devising novel aims. Over time, the catcher knows well what the thrower is capable of, whilst the thrower has attempted a wide spectrum of pitches.

In the algorithm, the thrower is represented by a forward model attached to a controller (policy) and the catcher a ``meta-model''. We unpack and report them individually. Procedural information is summarised in Algorithm \ref{alg:hhvg}.

\subsection{Forward model} \label{subsec:fm}

We start by specifying at current time the state and action sample as $\boldsymbol{s}$ and $\boldsymbol{a}$. The forward model describes the probability distribution over future state $S^\prime$, given $\boldsymbol{s}$, $\boldsymbol{a}$, and parameter $\theta$.
	\begin{equation}
	\begin{aligned} \label{eq:fm}
	P(S^\prime | A=\boldsymbol{a}, S=\boldsymbol{s}; \theta)
	\end{aligned}
	\end{equation}
The entropy associated with $S^\prime$, conditioned on $\boldsymbol{s}$ and $\boldsymbol{a}$, gives a measure of the degree to which $S^\prime$ is informative on average. We referred to this measure as one of {\it interestingness}. Note this is a different concept from the `interestingness' proposed by \citet{schmidhuber2008driven}, which is the first-order derivative of compressibility.

\subsection{Boredom, outcome devaluation, and meta-model} \label{subsec:boredom}

Boredom, in common understanding, is perhaps not unfamiliar to most under the situation of being exposed to certain information which one has known well by heart. It is the opposite of being interested. In the current work, we limited the exposure of information to those being disclosed by one's actions.

To mark the necessity of boredom, we first identify the limitation of a naive instantiation of curiosity; then, we show that the introduction of boredom serves to resolve this limitation.

Consider the joint occurrence of future state $S^\prime$ and action $A$: $ P (S^\prime, A|S=\boldsymbol{s}; \theta, \varphi)$. This is derived from product rule given Equation \ref{eq:fm} and action policy $\pi(A|S=\boldsymbol{s}; \varphi)$, parametrised by $\varphi$ (action policy is revisited in Section \ref{subsec:policy}).

A naive approach to curiosity is by optimising the action policy, such that $A$ is predictive of maximum {\it interestingness} (see Section \ref{subsec:fm}) about the future.

However, this approach would certainly lead to the agent behaving habitually and, as a consequence, becoming obsessive about a limited set of outcomes. In other words, a purely interestingness-seeking agent is a darkroom agent (see Section\ref{subsec:homeohetero}; also \citet{darkroom} for related concept).

The problem with the naively curious agent is that it perceives novelty as permanently novel. The agent has no recourse to inform itself via assimilating the information that brought about novelty. If the agent is otherwise endowed with the assimilation capacity, a sense of boredom would be induced. The induction of boredom essentially causes the agent to value the same piece of information less, thus changing the agent's perception towards interestingness. If the agent were to pursue the same interestingness-seeking policy, a downstream effect of boredom would drive the agent to seek out other information that could have been known. This conception amounts to an implicit goal of {\it devaluating} known outcomes.

To this end, we introduce the following meta-model $Q$ to represent {\it a priori} knowledge about the future. The meta-model, parametrised by $\psi$, is an approximation to the {\it true} marginalisation of joint probability $P(S^\prime, A|S=\boldsymbol{s}; \theta, \varphi)$ over $A$:
	\begin{equation}
	\begin{aligned} \label{eq:mm}
	Q(S^\prime | S=\boldsymbol{s}; \psi) & \approx
		P (S^\prime | S=\boldsymbol{s}; \theta, \varphi) \\
		&= \sum_A \Big[ P(S^\prime, A|\boldsymbol{s}; \theta, \varphi) \Bigr] \\
		&= \sum_A \Bigl[ P (S^\prime| A, \boldsymbol{s}; \theta) \pi(A|\boldsymbol{s}; \varphi) \Bigr] \\
	\end{aligned}
	\end{equation}

We associate the occurrence of boredom, or, synonymously, outcome devaluation, with minimising the devaluation objective with respect to $\psi$. The devaluation objective is given by the Kullback-Leibler (KL) divergence:
	\begin{equation}
	\begin{aligned} \label{eq:mm-loss}
	\mathcal{L}_{mm}(\psi) &:=
	\mathcal{L}(\boldsymbol{a}, \boldsymbol{s}; \psi, \theta) \\
		&\phantom{:}=
		D_{KL}\left[ P(\boldsymbol{s}^\prime|\boldsymbol{a}, \boldsymbol{s}; \theta)
		\middle\Vert
		Q(\boldsymbol{s}^\prime|\boldsymbol{s}; \psi) \right]
	\end{aligned}
	\end{equation}
For the notation $\mathcal{L}_{mm}(\psi)$, where $mm$ stands for meta-model, we have dropped the dependence of $\theta$, $\boldsymbol{s}$, and $\boldsymbol{a}$. This only serves to emphasise that optimising the devaluation objective is with respect to $\psi$.

\subsection{Devaluation progress, intrinsic reward, and value learning}

Through the use of KL-divergence in Equation \ref{eq:mm-loss}, we emphasise the complementary nature of devaluation in relation to a knowledge-gaining process. That is to say, devaluation results in information gain for the agent. This, in fact, can be regarded as cognitively rewarding and, thus, serves to motivate our definition of intrinsic reward.

One rewarding scenario happens when $Q(S^\prime|\boldsymbol{s}; \psi)$ has all the information there is to be possessed by $A$ about $S^\prime$. $A$ is therefore rendered redundant. One may speculate, at this point, the agent could opt for inhibiting its responses. Disengaging actions potentially saves energy which is rewarding in biological sense.

Alternatively, the agent may attempt to develop new behavioural repertoires, brining into $S^\prime$ new information (i.e., novel outcomes) that is otherwise unknown to $Q$. The ensuing sections will focus on this line of thinking.

From Equation \ref{eq:mm-loss}, we construct the quantity {\it devaluation progress} to represent an intrinsically motivated reward. The devaluation progress is given by the difference between KL-divergences before and after devaluation (as indicated by the superscript $(i+1)$):
	\begin{equation}
	\begin{aligned} \label{eq:deval-prog}
	R_\psi^{(i+1)}(\boldsymbol{a}, \boldsymbol{s})
		&:=
		\mathcal{L}(\boldsymbol{a}, \boldsymbol{s}; \psi^{(i)}, \theta) -
		\mathcal{L}(\boldsymbol{a}, \boldsymbol{s}; \psi^{(i+1)}, \theta) \\
		&\phantom{:}=
		\mathcal{L}_{mm}(\psi^{(i)}) - \mathcal{L}_{mm}(\psi^{(i+1)}),
	\end{aligned}
	\end{equation}
Here, we write $R_\psi^{(i+1)}(\boldsymbol{a}, \boldsymbol{s})$ in accordance with notational convention in reinforcement learning, where reward is typically a function of state and action. Subscript $\psi$ indicates the dependence of $R$ on meta model parameter.

Having established the intrinsic reward, value learning is such that the value function approximator $\hat{V}(\boldsymbol{s}; \nu)$ follows the Bellman equation $V(\boldsymbol{s}) = \mathbb{E}_{\boldsymbol{a}}[R(\boldsymbol{a}, \boldsymbol{s}) + \gamma \mathbb{E}_{\boldsymbol{s}^\prime}[V(\boldsymbol{s}^\prime)]]$. In practice, we minimise the objective with respect to $\nu$:
	\begin{equation}
	\begin{aligned} \label{eq:vf-loss}
	\mathcal{L}_{vf}(\nu)
		&:=
		\mathcal{L}(\boldsymbol{s}^\prime, \boldsymbol{a}, \boldsymbol{s}; \nu) \\
		&\phantom{:}=
		\norm{y - \hat{V}(\boldsymbol{s}; \nu) }^2 \\
		y
		&\phantom{:}=
		R_\psi^{(i+1)}(\boldsymbol{a}, \boldsymbol{s}) + \gamma \hat{V}(\boldsymbol{s}^\prime; \tilde\nu)
	\end{aligned}
	\end{equation}

\subsection{Policy optimisation} \label{subsec:policy}

We define action policy at state $S=\boldsymbol{s}$ as the probability distribution over $A$ with parameter $\varphi$:
	\begin{equation}
	\begin{aligned} \label{eq:policy}
	\pi(A|S=\boldsymbol{s}; \varphi)
	\end{aligned}
	\end{equation}

Our goal is to determine the policy parameter $\varphi$ that maximises the expected sum of future discounted rewards. One approach is by applying Stochastic Value Gradients \citep{svg} and maximises the value function. We thus define our policy objective as follows (notice the negative sign; we used a gradient update rule that defaults to minimisation):
	\begin{equation}
	\begin{aligned} \label{eq:policy-loss}
	\mathcal{L}_{ap}(\varphi)
		&:=
		\mathcal{L}(\boldsymbol{s}^\prime, \boldsymbol{a}, \boldsymbol{s}; \theta, \psi^{(i)}, \psi^{(i+1)}, \nu, \varphi) \\
		&\phantom{:}= -
		\mathbb{E}_{\boldsymbol{a} \sim \pi(\cdot | \boldsymbol{s}; \varphi)}
		\Bigl[
		R_\psi^{(i+1)}(\boldsymbol{a}, \boldsymbol{s}) + \gamma
		\mathbb{E}_{\boldsymbol{s}^\prime \sim P(\cdot |\boldsymbol{a}, \boldsymbol{s}; \theta)}
		\bigl[ \hat{V}(\boldsymbol{s}^\prime; \nu) \bigr]
		\Bigr] \\
	\end{aligned}
	\end{equation}

\subsection{Remarks on homeostatic and heterostatic regulations} \label{subsec:homeohetero}

\citet{intrinsicmotiv} outlined the distinctions between two important classes of intrinsic motivation: homeostatic and heterostatic. A homeostatic motivation is one that can be satiated, leading to certain equilibrium behaviourally; whereas a heterostatic motivation topples the agent, thus preventing it from occupying habitual states.

Our algorithm entails regulations relating to both classes of intrinsic motivation. Specifically, the devaluation objective (Equation \ref{eq:mm-loss}) realises the homeostatic aspect due to its connection with induced satiety. On the other hand, the devaluation progress (Equation \ref{eq:deval-prog}) introduced for policy optimisation instantiates a heterostatic drive to agent's behavioural pattern.

Heterostasis is motivated by the agent pushing itself towards novelty and away from devalued, homeostatic states (Equation \ref{eq:hetero}). We develop this statement more formally by first re-examining Equation \ref{eq:policy-loss}, with reference to Equation \ref{eq:deval-prog} and \ref{eq:mm-loss}. We arrived at the following form by admitting expected KL-divergence:
	\begin{equation}
	\begin{aligned}
	&\phantom{={}}
		-\mathbb{E}_{\boldsymbol{a} \sim \pi(\cdot | \boldsymbol{s};\varphi)}
		\Big[
		D_{KL}[P(\boldsymbol{s}^\prime|\boldsymbol{a}, \boldsymbol{s}; \theta) \Vert Q(\boldsymbol{s}^\prime|\boldsymbol{s};\psi^{(i  )})] -
		D_{KL}[P(\boldsymbol{s}^\prime|\boldsymbol{a}, \boldsymbol{s}; \theta) \Vert Q(\boldsymbol{s}^\prime|\boldsymbol{s};\psi^{(i+1)})]
		\Big] \\
	&\phantom{={}}
		- \mathbb{E}_{\boldsymbol{a}\sim\pi(\cdot|\boldsymbol{s};\varphi)}
		  \mathbb{E}_{\boldsymbol{s}^\prime \sim P(\cdot | \boldsymbol{a}, \boldsymbol{s}; \theta)}
		  \Big[ V(\boldsymbol{s}^\prime; \nu) \Big] \\
	&=
		- \Bigl\{
		I(S^\prime : A|S=\boldsymbol{s}; \psi^{(i)}, \varphi, \theta) -
		I(S^\prime : A|S=\boldsymbol{s}; \psi^{(i+1)}, \varphi, \theta)
		\Bigr. \\
	&\phantom{={}-\Big.}
		+ \Bigl.
		\mathbb{E}_{\boldsymbol{a}\sim\pi(\cdot|\boldsymbol{s};\varphi)}
		\mathbb{E}_{\boldsymbol{s}^\prime \sim P(\cdot | \boldsymbol{a}, \boldsymbol{s}; \theta)}
		\big[ V(\boldsymbol{s}^\prime; \nu) \big]
		\Bigr\}
	\end{aligned}
	\end{equation}

Notice that the expected devaluation progress becomes the difference between conditional mutual information $I$ before ($\psi^{(i)}$) and after devaluation ($\psi^{(i+1)})$.

Assume, for the moment, that the agent is equipped with devaluation capacity only. In other words, we replace the devaluation progress and fall back on devaluation objective, $R := \mathcal{L}_{mm}(\psi)$ (cf. Equation \ref{eq:deval-prog}). The agent is now interestingness-seeking with homeostatic regulation. We further suppose that the dynamics of $\psi$ and $\varphi$ evolve in tandem, which gives
	\begin{equation}
	\begin{aligned} \label{eq:pardyn}
	I(S^\prime : A | S=\boldsymbol{s}; \psi^{(i)}, \varphi^{(k)}) &\to I(S^\prime : A | S=\boldsymbol{s}; \psi^{(i+1)}, \varphi^{(k)}) \\
		&\to I(S^\prime : A | S=\boldsymbol{s}; \psi^{(i+1)}, \varphi^{(k+1)}) \\
		&\to I(S^\prime : A | S=\boldsymbol{s}; \psi^{(i+2)}, \varphi^{(k+1)}) \to \dots
	\end{aligned}
	\end{equation}
In practice, the nature of devaluation and policy optimisation often depends on replaying agent's experience. Taking turn applying gradient updates to $\psi$ and $\varphi$ creates a self-reinforcing cycle that drives the policy to converge towards a point mass. For instance, if the policy is modelled by some Gaussian distribution, this updating scheme would result in infinite precision (zero spread).

For curiosity, however, such parameter dynamics should not be catastrophic if we subsume the homeostatic regulation and ensure the preservation of the relation given in Equation \ref{eq:mutinfo-relation}:
	\begin{equation}
	\begin{aligned} \label{eq:mutinfo-relation}
	I(S^\prime : A | S=\boldsymbol{s}; \psi^{(i+1)}, \varphi^{(k)}) \le
		I(S^\prime : A | S=\boldsymbol{s}; \psi^{(i)}, \varphi^{(k)}) &\le
		I(S^\prime : A | S=\boldsymbol{s}; \psi^{(i+1)}, \varphi^{(k+1)}) \\
	\Rightarrow
		-I(S^\prime : A | S=\boldsymbol{s}; \psi^{(i+1)}, \varphi^{(k)}) +
		 I(S^\prime : A | S=\boldsymbol{s}; \psi^{(i)}, \varphi^{(k)}) &\le
		 I(S^\prime : A | S=\boldsymbol{s}; \psi^{(i+1)}, \varphi^{(k+1)})
	\end{aligned}
	\end{equation}
This equation holds because the devaluation process on average has a tendency to make $A$ less informative about $S^\prime$, after which $A$ is perturbed to encourage a new $S^\prime$ less predictable to $Q$. By rearranging the equation such that the left hand side remains positive, we have arrived at a lower bound on $I(S^\prime : A | S=\boldsymbol{s}; \psi^{(i+1)}, \varphi^{(k+1)})$ which recovers the expected devaluation progress.

Equation \ref{eq:argneq} summarises the argument associated with Equation \ref{eq:mutinfo-relation} and \ref{eq:pardyn}.
	\begin{equation}
	\begin{aligned} \label{eq:argneq}
	\varphi^{(k+1)}
	&=
	\arg\max_{\varphi^{(k)}}
	\Bigl[
	I(S^\prime : A|S=\boldsymbol{s}; \psi^{(i)}, \varphi^{(k)}) -
	\min_{\bar{\psi}^{(i)}} I(S^\prime : A|S=\boldsymbol{s}; \bar{\psi}^{(i)}, \varphi^{(k)})
	\Bigr] \\
	&\neq
	\arg\max_{\varphi^{(k)}}
	\Bigl[
	\min_{\psi^{(i)}} I(S^\prime : A|S=\boldsymbol{s}; \psi^{(i)}, \varphi^{(k)})
	\Bigr]
	\end{aligned}
	\end{equation}

Finally, we offer an intuition on how policy optimisation gives rise to heterostatic motivation. This is made clear from the optimised target $I(S^\prime : A | S=\boldsymbol{s}; \psi^{(i+1)}, \varphi^{(k+1)})$, found on the right hand side of Equation \ref{eq:mutinfo-relation}. It is instructive to re-introduce the true marginalisation $P(S^\prime|S=\boldsymbol{s}; \theta, \varphi)$ from Equation \ref{eq:mm}; write:
	\begin{equation}
	\begin{aligned} \label{eq:hetero}
	&\phantom{=..}
	I(S^\prime : A|S=\boldsymbol{s}; \psi^{(i+1)}, \varphi^{(k+1)}) \\
	&=
	\sum_{\boldsymbol{a}} \pi(\boldsymbol{a}|\boldsymbol{s}; \varphi^{(k+1)})
	\sum_{\boldsymbol{s}^\prime} P(\boldsymbol{s}^\prime|\boldsymbol{s}, \boldsymbol{a}; \theta)
	\log\frac{P(\boldsymbol{s}^\prime|\boldsymbol{a}, \boldsymbol{s}; \theta)}{Q(\boldsymbol{s}^\prime|\boldsymbol{s}; \psi^{(i+1)})} \\
	&=
	\sum_{\boldsymbol{a}} \pi(\boldsymbol{a}|\boldsymbol{s}; \varphi^{(k+1)})
	\sum_{\boldsymbol{s}^\prime} P(\boldsymbol{s}^\prime|\boldsymbol{s}, \boldsymbol{a}; \theta)
	\log
	\frac{P(\boldsymbol{s}^\prime|\boldsymbol{a}, \boldsymbol{s}; \theta)}  {P(\boldsymbol{s}^\prime|\boldsymbol{s}; \theta, \varphi^{(k+1)})}
	\frac{P(\boldsymbol{s}^\prime|\boldsymbol{s}; \theta, \varphi^{(k+1)})}  {Q(\boldsymbol{s}^\prime|\boldsymbol{s}; \psi^{(i+1)})} \\
	&=
	I(S^\prime : A|S=\boldsymbol{s}; \varphi^{(k+1)}) +
	D_{KL} \bigr[
	P(\boldsymbol{s}^\prime|\boldsymbol{s}; \theta, \varphi^{(k+1)})
	\Vert
	Q(\boldsymbol{s}^\prime|\boldsymbol{s}; \psi^{(i+1)}) \bigl]
	\end{aligned}
	\end{equation}
Simply, the optimised policy is such that the agent increases the conditional mutual information and is pushed away (via increasing the KL-divergence) from its homeostatic state $Q$.

\section{Implementation Considerations} \label{sec:impc}

This section presents practical considerations when motivating the aforementioned agent using neural networks. These considerations were mainly for the ease of calculating KL-divergence analytically.

\subsection{Forward model}

We assumed, at the any given time, the state follows some Gaussian distribution with mean $\boldsymbol{s}$ and covariance $\Sigma$. The future state is described by its mean $\boldsymbol{s}^\prime$ according to the deterministic mapping $\boldsymbol{s}^\prime = f(\boldsymbol{a}, \boldsymbol{s}; \theta)$, where $\boldsymbol{a}$ is the action sampled from policy. $f$ represents a neural network with trainable parameter $\theta$:
	\begin{equation}
	\begin{aligned} \label{eq:imp-fm}
	f(\boldsymbol{a}, \boldsymbol{s}; \theta) &= \boldsymbol{A} \boldsymbol{s} +
	\left( \sum_\iota a_{\iota} \boldsymbol{B}^{\iota} \right) \boldsymbol{s} +
	\boldsymbol{C} \boldsymbol{a} + o
	\end{aligned}
	\end{equation}
$\boldsymbol{A}$, $\boldsymbol{B}$, and $\boldsymbol{C}$ are approximations of Jacobian matrices and $o$ a constant, all depending on $\theta$. $\boldsymbol{B}$ is a three-way tensor indexed by $\iota$ along the first axis. This treatment is similar to \citet{e2c} (also cf. \citet{dvbf}), except that we considered a bilinear approximation and that, in the following sections, we used only the mean states in a deterministic environment.

The above formalism follows that $\boldsymbol{s}^\prime$ has covariance matrix $\mathbb{E}[\boldsymbol{s}\boldsymbol{s}^{\prime\intercal}] = \boldsymbol{J} \boldsymbol{\Sigma} \boldsymbol{J}^\intercal$, where $\boldsymbol{J} = \left( \boldsymbol{A} + \sum_\iota a_\iota \boldsymbol{B}^\iota \right)$. The transition probability is then given by
	\begin{equation}
	\begin{aligned} \label{eq:imp-fmcov}
	P(S^\prime | A=\boldsymbol{a}, S=\boldsymbol{s}; \theta) &= N \left(f(\boldsymbol{a}, \boldsymbol{s}; \theta), \boldsymbol{J} \boldsymbol{\Sigma} \boldsymbol{J}^\intercal \right)
	\end{aligned}
	\end{equation}

\subsection{Meta model}

Our meta model was defined as $Q(S^\prime|S=\boldsymbol{s}; \psi) = N(\boldsymbol{\mu}^\prime, \boldsymbol{\Sigma}^\prime; \psi)$, where mean $\boldsymbol{\mu}^\prime$ and covariance matrix $\boldsymbol{\Sigma}^\prime$ are outputs of a neural network parametrised by $\psi$. Specifically, the covariance matrix is constructed as follows:
	\begin{equation}
	\begin{aligned} \label{eq:imp-mmcov}
	\boldsymbol{\Sigma}^\prime &= \boldsymbol{H} \boldsymbol{D} \boldsymbol{H}^\intercal, \;\; \boldsymbol{D} = \mathrm{diag}(\boldsymbol{d}) \\
	\boldsymbol{H} &= \boldsymbol{I} - 2\frac{\boldsymbol{vv}^\intercal}{\norm{\boldsymbol{v}}^2},
	\end{aligned}
	\end{equation}
where $\boldsymbol{d}$ is a positive-valued vector, $\boldsymbol{I}$ an identity matrix, and $\boldsymbol{v}$ a Householder vector \citep{householderflow}.

Whenever possible, e.g., employing Experience Replay, gradients of the objective may be weighted by the probability ratio $P(\boldsymbol{s}^\prime|\boldsymbol{a}, \boldsymbol{s}; \theta^{(\ell+1)}) / {P(\boldsymbol{s}^\prime|\boldsymbol{a}, \boldsymbol{s}; \theta^{(\ell)})}$, where the superscripts $(\ell+1)$ and $(\ell)$ indicate forward model parameters after and before gradient updates. This procedure encourages boredom to be properly induced in accordance with forward model learning.

\section{Experiment}

To verify whether our algorithm exhibits online curiosity, we focused on benchmarking agent's forward model under these constraints: i) agent should learn to bootstrap its own training set; ii) the probability of visiting different states is not uniformly distributed; iii) the amount of time to accumulate training data points is limited.

Boredom-based benchmarks were compared 1) against (ideal) models trained using oracle dataset, and 2) with reduced models under the pruning hierarchy (Section \ref{subsec:mod-prune}, Table \ref{tab:model-prune}).

\subsection{Training environment}

Our agents were tested in a physics simulator, free of stochasticity, built to expand the classical Mountain Car environment (e.g., `MountainCar-v0' included in \citet{aigym}) into two-dimensional state space. The environment is analogous to the Mountain Car in ways that it has attractors and repellers that resemble hill- and valley-like landscapes (Figure \ref{fig:env}). The presence of both structures serves as acceleration modifier to the agent. This makes state visitation biased toward attractors. Therefore, the acquisition of an accurate forward model necessitates planning visits to the vicinity of repellers.

The states an agent can occupy were defined as the tuple $(x, y, \dot x, \dot y)$ in continuous real space. Positions $(x, y) \in [0, 1]^2$ were bounded in a unit square, whereas velocities $(\dot x, \dot y)$ were not. Boundary condition resets $x$ and $y$ to zero velocities. However, it is possible for the agent to slide along the boundaries if its action goes in the direction parallel to the nearby boundary. We note that being trapped in the corners is possible; though an agent could potentially get itself unstuck if appropriate actions were carried out.

Agent's action policy was represented by a categorical distribution over accelerations in $x$ and $y$ directions. The distribution was defined on the interval $[-2.0, 2.0]^2$, evenly divided into a $11\times 11$ grid. When an action is selected, the corresponding acceleration is modified according to forces exerted by the attractors and repellers.

Unlike the classical Mountain Car, our environment does not express external rewards, nor does it possess any states that are indicative of termination. Agents were allowed a pre-defined time limit ($T=30,000$ steps; {\it Data Accumulation Phase} or DAP) to act without interruption. Agent's experiences in terms of state transitions were collected in a database, which was sampled from for training at each step. During DAP, learning rates for model parameters remained constant. After DAP (or {\it post}-DAP), agent entered an action-free stage lasted for $T=30,000$, during which only sampling from own experience pool for forward model training was performed. Learning rate scheduling scheme was implemented at post-DAP.

\subsection{Oracle dataset}

To contrast with self-assisted data accumulation, we constructed an oracle dataset, with which a (forward) model was trained. We referred to this class of model as Oracle. The oracle dataset assumed unbiased state occupancy and action choice. Specifically, we acquired the dataset by evenly dividing the state-action space into a $49 \times 49 \times 11 \times 11 \times 11 \times 11$ grid. Each state-action pair was passed to the physics simulator to evaluate the future state. The oracle dataset differs from self-assisted ones in that contained positions near the repellers that an agent is incapable of visiting.

The training, testing, and validation sets were prepared by re-sampling the resulting dataset without replacement according to the ratio $0.8$, $0.16$, and $0.04$. The model was trained for $60,000$ epochs. During training, the learning rate was scheduled according to test error. Benchmarking was performed on the validation set as part of model comparisons (see Section \ref{subsec:modcompare}).

\subsection{Model pruning} \label{subsec:mod-prune}

We defined five variants of our boredom-driven curious agent. With each variation, the agent receives cumulative reductions in network components. Theses reductions are summarised as model pruning hierarchy in Table \ref{tab:model-prune}.

The reason that we motivated model comparisons based on model pruning is as follows. Overall, as model pruning progresses the agent was deprived of connections with constructs like devaluation progress, intrinsic motivation, and planning. Eventually, the agent lost the ability to contextualise action selection and became a random-walk object. At the stage, the agent was still explorative in a crude sense, albeit not in a curious way. Through these treatments we demonstrated the impact boredom and intrinsic motivation have on model learning.

\subsubsection{Boredom-driven curiosity (C/B)}

The first agent variant retained all distinctive components introduced in Section \ref{sec:algorithm}. The meta-model provides the devaluation progress as intrinsic rewards, whilst the value function enables the agent to plan actions that are intrinsically rewarding in the long run.

\subsubsection{Predictive error-driven curiosity (C/PE)}

The C/PE variant tests whether the induction of boredom is a constructive form of intrinsic motivation. This is achieved by removing the meta-model, thereby requiring an alternative definition of intrinsic reward. We replaced the devaluation progress with {\it learning progress} defined by mean squared errors of the forward model:
	\begin{equation}
	\begin{aligned}
	R_\theta^{(\ell + 1)}
	&:=
	\mathcal{L}_{fm}(\theta^{(\ell)}) - \mathcal{L}_{fm}(\theta^{(\ell+1)}) \\
	\mathcal{L}_{fm}(\theta)
	&:=
	\mathcal{L}(\boldsymbol{s}^\prime, \boldsymbol{a}, \boldsymbol{s}; \theta) \\
	&\phantom{:}=
	\Vert \boldsymbol{s}^\prime - f(\boldsymbol{a}, \boldsymbol{s}; \theta) \Vert^2
	\end{aligned}
	\end{equation}
The construction of learning progress is one typical approach to intrinsic motivation and curiosity \citep{pathak2017icm, schmidhuber1991}.

\subsubsection{Policy gradients, intrinsic reward samples (PG/IRS), Gaussian rewards (PG/GR)}

Next, we examined how reward statistics alone influences policy update and, as a consequence, model learning. The value function was removed at this stage to dissociate policy learning from any downstream effects of value learning.

One distinctive feature of devaluation progress is that it entails time-varying rewards --- depending on the amount of time over which an agent has evolved in the environment. We hypothesised that the emergence of curious policy is associated with reward dynamics over time. That is to say, if one perturbs the magnitudes and directions of the policy gradients with reward statistics appropriate for the ongoing time frame, the agent should exhibit similar curious behaviours. Nevertheless, we argue that such treatment is only sensible given virtually identical initial conditions. Specifically, all agent variants shared the same, environmental configuration, initial position, and network initialisation.

To this end, we prepared a database for intrinsic reward samples. During C/B performance, all reward samples were collected and labelled with the corresponding time step. Afterwards, the PG/IRS agents randomly sampled from the database in a temporally synchronised manner and applied standard policy gradients.

The PG/IRS was contrasted with the PG/GR variant. Their difference lies in that a surrogate reward was used in place of the database. We defined the surrogate reward as a Gaussian distribution with time-invariant parameters, in which the mean $\mu=0$ is under the assumption of equilibrium devaluation progress and the standard deviation $\sigma=0.01$, as derived from the entire database.

\subsubsection{Random-walk policy (P/RW)}

Finally, we constructed a random-walk agent. All network components, apart from the forward model, were removed. This agent variant represents the case without intrinsic motivation and is agnostic to curiosity. Broadly speaking, the agent was still explorative due to its maximum entropy action policy. We regarded this version as the worse case scenario to contrast with the rest of the variants.

\subsection{Model comparisons} \label{subsec:modcompare}

All model variants were compared on the basis of validation error given the oracle dataset. We performed 128 runs for each of the six variants (Oracle, C/B, C/PE, PG/IRS, PG/GR, and P/RW). All variants, across all runs, were assigned to identical environmental configuration (e.g., initial position, attractor/repeller placements). Network components, whenever applicable, shared identical architecture and were trained with consistent batch size and learning rate. Model parameters followed the Xavier initialisation \citep{glorot}. During post-DAP, learning rate scheduling was implemented such that a factor $0.1$ reduction was applied upon a 3000-epoch loss plateau.

\section{Results}

We first characterised individual agent variants' qualities of being i) explorative and ii) perseverative. Active exploration is one defining attribute of curiosity \citep{2013gottlieb}, simply because it differentiates between uncertain and known situations, thus giving rise to effective information acquisition. This, however, should be complemented with bounded perseverance; namely, to prevent oneself from being permanently or dynamically captured---i.e., by the corners or the attractor.

The two qualities can be distinguished, as shown in Figure \ref{fig:covent}, by respective measures of Coverage Rate (CR) and Coverage Entropy (CE). The two measures were computed by first turning the state space into a $50\times 50$ grid, ignoring velocities. CR then marks over time whether or not a cell has been visited. Whereas, CE treats the grid as a probability distribution. Starting with maximum entropy, CR cumulatively counts the number of times a position is being visited. Entropy was calculated at each time step using normalised counter.

Because (state) visitation bias was inherent in our testing environment, naturally, agents occupying a subset of states would cause CE to reduce faster than those who attempted to escape. The C/B, C/PE, and PG/IRS variants were regarded as curious and intrinsically motivated. Our results showed that these variants were predominantly explorative and non-perseverative. By contrast, the P/RW agent, albeit explorative, had no principled means to escape the dynamic lock. However, if $t\to\infty$ the P/RW should be able to explore further by chance. The PG/GR variant, on the other hand, exhibited, intermediate explorativeness and extreme perseverance with disproportionately high variance. We attributed this behaviour to the detrimental effects of inappropriately informative reward statistics.

Next, we benchmarked forward model performance of individual variants by their validation loss and error percentage. We reported DAP and post-DAP performances separately as a function of time in Figure \ref{fig:vldprogress}. Error percentage was calculated as the percent ratio between root mean squared loss and the maximum pair-wise Euclidean distance in the validation set.

The Oracle model, trained under the supervision of oracle training set, reached an error percentage of $0.84\%$ for both DAP and post-DAP, amounting to approximately 30\% improvement over the terminal performance of C/B variant. All variants considered curious (C/B, C/PE, and PG/IRS) had similar performances during DAP. In particular, the PG/IRS, which received independent intervention from the `true' reward distributions achieved marginally lower performance but indistinguishable from the C/PE variant. This outcome was observed for both DAP and post-DAP, suggesting intrinsic reward samples derived from C/B contributed favourably even to the standard policy gradients algorithm.

Though without the ability to approximate value function, the PG/IRS variant underperformed in benchmarking, as compared with the value-enabled, C/B variant. Using non-parametric test, the difference was detected for DAP ($p=$ 0.0006) and post-DAP ($p=$ 6.4E-8), respectively. Similar observations were also made for comparisons between C/B and C/PE, at $p=$ 0.0029 (DAP) and $p=$ 5.9E-5 (post-DAP). Overall, this suggested significant differences in the experiences accumulated across agent variants. The aforementioned statistics were reported in Table \ref{tab:vldprogress} and \ref{tab:stats}.

\section{Conclusion}

We have provided a formal account on the emergence of boredom from an information-seeking perspective and addressed its constructive role in enabling curious behaviour. We envisaged actions as instrumental in agent's epistemic disclosure, which is assimilated by another conditionally independent cognitive process. This led to the central claim of this study---pertaining to superior data-gathering efficiency and hence effective curiosity. We supported this claim with empirical evidence, showing that boredom-enabled agents consistently outperformed other curious agents in self-assisted world model learning. Our results solicited the interpretation that the relationship between homeostatic and heterostatic intrinsic motivations can in fact be complementary; therefore, we have offered one unifying perspective for the intrinsic motivation landscape.

\section*{Conflict of Interest Statement}

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. All authors were employed by Araya, Inc.

\section*{Author Contributions}
YY conceived of this study, performed the experiments, and wrote the first draft of the manuscript. AYCC programmed the physics simulator, wrote part of Introduction, and created Figure \ref{fig:intuition}. All authors contributed to manuscript revision, read and approved the submitted version.

\section*{Funding}
This study was funded by the Japan Science and Technology Agency (JST) under CREST grant number JPMJCR15E2.

\section*{Acknowledgments}
YY would like to thank Martin Biehl and Ildefons Magrans de Abril for insightful discussions.

\bibliographystyle{plainnat}

redpajama_set_name: RedPajamaArXiv


text:
Marchetti ist der Familienname folgender Personen:

 Andrew Anthony Marchetti (1901–1970), US-amerikanischer Gynäkologe
 Cesare Marchetti (* 1927), italienischer Physiker
 Cristina Marchetti (* 1955), italienische Physikerin
 Domenico de Marchetti (1626–1688), italienischer Anatom
 Enrico Marchetti, italienischer Geigenbauer
 Federico Marchetti (* 1983), italienischer Fußballspieler
 Fermo Dante Marchetti (1876–1940), italienischer Komponist und Musiker
 Filippo Marchetti (1831–1902), italienischer Komponist
 Francesco Marchetti Selvaggiani (1871–1951), italienischer Kurienkardinal

 Gianpietro Marchetti (* 1948), italienischer Fußballspieler
 Gino Marchetti (1927–2019), US-amerikanischer American-Football-Spieler
 Giorgia Marchetti (* 1995), italienische Tennisspielerin

 Giulio Marchetti (1911–1993), italienischer Schauspieler
 Nico Marchetti (* 1990), österreichischer Politiker (ÖVP)
 Nino Marchetti (1905–1983), italienischer Schauspieler
 Pedro Antônio Marchetti Fedalto (* 1926), brasilianischer Geistlicher, Alterzbischof von Curitiba
 Stefano Marchetti (* 1986), italienischer Eishockeyspieler
 Tiba Marchetti (* 1967), österreichische Fernsehmoderatorin
 Vicente Ângelo José Marchetti Zioni (1911–2007), römisch-katholischer Erzbischof von Botucatu, Brasilien
 Vincent Marchetti (* 1997), französischer Fußballspieler

Sonstiges:
 Marchetti-Gletscher, Gletscher im Viktorialand, Antarktika

redpajama_set_name: RedPajamaWikipedia


Yess! And, I found a really cute sundress at Value Village last week. Brand new from the Gap, tags and stickers still on it for $39.99. I got it for less than $17!
So, I went to Value Village again yesterday...found 10 great deals, and even a shirt there that I had when I was in 8th grade! LOL! An American Eagle 3/4 sleeve polo. I laughed really hard, and thought of you!


text:
Policies / Forms
School Quiz
Young Programmers
Active Week
St. Pius X Primary schools were founded in 1965. We had our humble beginnings in six pre-fabricated classrooms erected on grounds leased from St. Mary's rugby club which then adjoined the church of St. Pius X. On 15th February 1965 the schools opened their doors to 176 children. As was the tradition at the time there were separate schools for boys and girls on the same campus. The boys were very much in the majority numbering 117 while the number of girls totalled 59. On 19th January 1971 the new Primary schools of St. Pius X were officially opened and blessed by the Most Rev. Dr. J. C. McQuaid. The new schools had 16 classrooms each and on that memorable day in 1971 there were 28 teachers catering for 1130 children.
On 30th September 2005, St. Pius schools celebrated their 40 year anniversary with a Gala night in the Burlington hotel which was attended by past pupils, parents, friends, associates, staff and former staff members. A wonderful night was enjoyed by all. A wonderful 40th year was rounded off by a visit from President Mary MacAleese to the school in December 2005 where she unveiled a plaque to commemorate the occasion. The school now has 24 full time teachers and a population of 480 boys. It has continued to develop and grow. In 2006, a new building, comprising a science room, a large general purpose room and five resource rooms, was opened.
SPORTING HISTORY
St. Pius has a rich history of partipation and success in a wide variety of sports. Whilst there is a 'leaning' towards GAA sports, the boys have also participated in swimming, Olympic handball, rugby, soccer, cricket, golf and athletics. As recently as 2007, the boys made the final of the Cumann na mBunscoil Frank Cahill Cup. A dedicated PE teacher has been employed since 1990 and she has been assisted over the years by staff members and visiting coaches.
MUSICAL HERITAGE
St. Pius takes pride in its rich musical heritage. The school choir has participated in many competitions and recitals, both national and international. It has won a plethora of prizes and plaudits over the years. Among its many engagements in the school year are its performances at the school confirmation and first communion ceremonies.
PAST PRINCIPALS
1965-1975 Mr. O'Doherty
1975-1998 Mr. Paddy O'Brien
1998-2002 Mr. Michael Quinn
2002 - 2017 Mr. Dermot Lynch
2017 - present Mr. Lorcan Coyle
Confirmation & Communion Classes
Confirmation Class 2019
Communion Class 2019
Communion 2012

redpajama_set_name: RedPajamaCommonCrawl


The World Book Day App is available now for iPhone, iPad and iPod touch on the iTunes store, all for the low, low price of absolutely FREE!
Destiny brought them together. The Gods will keep them apart.
When shy, awkward Helen Hamilton sees Lucas Delos for the first time she thinks two things: the first, that he is the most ridiculously beautiful boy she has seen in her life; the second, that she wants to kill him with her bare hands.
With an ancient curse making them loathe one another, Lucas and Helen have to keep their distance. But sometimes love is stronger than hate, and not even the gods themselves can prevent what will happen . . .
Josephine Angelini is a Massachusetts native and the youngest of eight siblings. A real-live farmer's daughter, Josie graduated from New York University's Tisch School of the Arts in theatre, with a focus on the classics. She now lives in Los Angeles with her screenwriter husband . . . and she can still drive a tractor..
Browse and buy books by Josephine Angelini here.
From the author of Incarceron comes a brand new stunning fantasy, Obsidian Mirror.
Jake's father disappears while working with the secretive and mysterious Oberon Venn – and Jake is certain Venn has murdered him.
But what he finds at the snow-bound Wintercome Abbey is far stranger.
A hidden diary of lost secrets.
A tribe of supernatural beings who haunt the woods.
A girl pursued by a wolf of ice.
There are life-threatening choices at every turn. But Jake won't stop until he knows the truth.
Catherine Fisher is an award-winning fantasy writer and author of the New York Times bestseller Incarceron.
Author of many books for children and two volumes of award-winning poetry, she has been named as the first ever Welsh Young People's Laureate.
Browse and buy books by Catherine Fisher here.
The sickness destroyed everyone over the age of fourteen. All across London diseased adults are waiting, hungry predators with rotten flesh and ravaged minds.
Small Sam and his unlikely ally, The Kid, have survived. They're safe with Ed and his friends at the Tower of London, but Sam is desperate to find his sister.
Their search for Ella means Sam and The Kid must cross the forbidden zone. And what awaits them there is more terrifying than any of the horror they've suffered so far . . .
Charlie Higson is a well-known writer of screenplays and novels, and is the author of the bestselling Young Bond series. He is also a big fan of horror films and is hoping to give many readers sleepless nights with his successful zombie series, The Enemy.
Browse and buy books by Charlie Higson here.
The first book in the bestselling fantasy series The Power of Five by Anthony Horowitz.
Anthony Horowitz is one of the most popular contemporary children's writers, and his Alex Rider series is enjoyed by millions of readers worldwide. He is also the author of the highly acclaimed Diamond Brothers detective stories, as well as the bestselling The Power of Five books. Anthony has won many major awards, including the Bookseller Association/Nielsen Author of the Year Award, the Children's Book of the Year Award at the British Book Awards and the Red House Children's Book Award. He also writes extensively for TV, with programmes including Foyle's War, Midsomer Murders and most recently Collision. Anthony lives in London.
Browse and buy books by Anthony Horowitz here.
With her rare ability to breathe fire, Jacinda is special among the draki – the descendants of dragons with the power to shift into human form.
But when Jacinda's rebelliousness forces her family to flee to the human world, her draki spirit starts to fade. If it is lost, she will remain as a human. The only thing that revives it is gorgeous, elusive Will . . . whose family hunts her kind.
Jacinda can't resist getting closer to him. Even if it means risking the draki's most closely-guarded secret – and her life.
Sophie Jordan grew up in the Texas Hill Country, where she wove stories of dragons, warriors, and princesses. A former high school English teacher, she is also a New York Times and USA Today best-selling author. She lives in Houston with her family, and when she is not writing she enjoys true crime and reality TV shows.
Browse and buy books by Sophie Jordan here.
In the dark, filthy salt mines of Endovier, an eighteen-year-old girl is serving a life sentence. She is a trained assassin, the best of her kind ñ but she made a fatal mistake. She got caught.
Young Captain Westfall offers her a deal: her freedom in return for one huge sacrifice. Celaena must represent the prince in a to-the-death tournament – fighting the most gifted assassins in the land. Live or die, Celaena will be free. Win or lose, she is about to discover her true destiny.
Throne of Glass is the first full-length novel by Sarah J. Maas to be published, though she already has a large online fan base, many of whom discovered the Throne of Glass world in its first incarnation, on www.fictionpress.com, when Sarah was just sixteen. To find out more, visit www.sjmaas.livejournal.com.
Browse and buy books by Sarah J. Maas here.
From the New York Times bestselling author of the Wicked Lovely series, Melissa Marr creates an extraordinary new world, running alongside our everyday world that will have even non-fantasy lovers begging for a sequel!
Head over to www.enterthecarnival.com for all the news, updates and loads of fun stuff.
Melissa Marr was voted in high school the ìmost likely to end up in jailî. Instead, she went to graduate school, worked in a bar, became a teacher and did a lot of writing. Her novels include Wicked Lovely, Ink Exchange, Fragile Eternity and Radiant Shadows.
Browse and buy books by Melissa Marr here.
No one knows what really happened the night Echo Emerson went from popular girl with jock boyfriend to gossiped-about outsider with 'freaky' scars on her arms. Even Echo can't remember the whole truth of the horrible night that changed her life forever.
Katie McGarry was a teenager during the era of grunge and boy bands and remembers those years as the best of her life. Writing, as Katie often told her best friend, is cheaper than therapy. Pushing the Limits is her debut novel. Visit Katie's website at www.katielmcgarry.com.
Browse and buy books by Katie McGarry here.
Sophie McKenzie was born and brought up in London, where she still lives with her teenage son. She has worked as a journalist and a creative writing teacher, and now writes full time. Her debut was the multi-award winning Girl, Missing (2006), and she is also the author of Blood Ties and sequel, Blood Ransom, The Medusa Project series, and the Luke and Eve trilogy. She has tallied up numerous award wins and has twice been longlisted for the Carnegie Medal.
Browse and buy books by Sophie McKenzie here.
Dodger is a tosher – a sewer scavenger living in the squalor of Dickensian London.
Everyone who is nobody knows Dodger. Anyone who is anybody doesn't.
But when he rescues a young girl from a beating, suddenly everybody wants to know him.
And Dodger's tale of skulduggery, dark plans and even darker deeds begins . . .
Terry Pratchett is the acclaimed creator of the global bestselling Discworld series, the first of which, The Colour of Magic, was published in 1983. In all, he is the author of over fifty bestselling books. His novels have been widely adapted for stage and screen, and he is the winner of multiple prizes, including the Carnegie Medal, as well as being awarded a knighthood for services to literature.
Browse and buy books by Terry Pratchett here.


text:
Majority of Asia/Pacific organizations struggle to cross the cloud maturity chasm and gain agility: IDC
Organizations can gain maturity through externally sourced services like IT outsourcing and managed services
According to IDC Asia/Pacific's latest study titled IDC MaturityScape Benchmark: Cloud in Asia/Pacific (excluding Japan), 2018, more than 85% of the Asia/Pacific organizations are still in the early stages of maturity, which in IDC MaturityScape's model are referred to as ad hoc and opportunistic stages. Compared to the previous Cloud MaturityScape benchmark study in 2016, over 20% of organizations has progressed from stage one (adhoc) to two (opportunistic). Fewer organizations are seen to have crossed over from stage two to three (repeatable), which is reflective of the difficulty in crossing the chasm between these two stages.
Progressing to stage three requires significant investments in technology, tools, processes and skills – all of which take time and budget. Internal competencies and skills to support these activities are also relatively low in the region, with most of the skilled resources concentrated in large multi-national enterprises and IT service providers. Thus, organizations that leverage on externally sourced services like IT outsourcing and managed services are the ones who are able to cross this maturity chasm. This is also a key contributing factor to the maturity in cloud adoption seen in developed countries like Australia, New Zealand and Singapore.
IDC believes that the technology gaps between 'Thrivers' and 'Survivors' organizations are narrowing because of the maturing technology which results in standardization of cloud services and their ease of adoption. But to progress to more advanced stages in the maturity curve, organizations must adopt a more strategic view of cloud as an enabling technology for business transformation and innovation. Faster time to value and agility is the new driver for cloud adoption. Cost of cloud adoption is rarely justified in the early stage of maturity as the adoption is primarily driven-based on required functionalities from the business.
"Speed and agility has become the key drivers for cloud adoption. More organizations in Asia Pacific are adopting a cloud-first strategy such that cloud infrastructure has now become a preferred option for IT modernization," according to William Lee, Research Director, Cloud Services, IDC Asia/Pacific. "Organizations need more consistent, standardized, and available automated cloud resources to enable developers and LOB teams to execute at speed and cost. Workload portability and application delivery across multiple clouds will be key to build a robust cloud services delivery platform for agility."
This study is a supplement to IDC MaturityScape: Cloud 2.0. These two reports provide a comprehensive overview of IDC's MaturityScape framework for cloud, which aims to assist organizations in realistically appraising their current capabilities, articulating reasonable short- and long-term goals, identifying their gaps, and beginning an action plan for change. The IDC MaturityScape specifically defines the stages, critical measures, business outcomes, and actions that are required to effectively move through the stages and increase value as more investments are made. Using the maturity measures and descriptions in the document, organizations can:
Understand exactly what sets 'thriving' organizations apart from 'surviving' organizations, based on their maturity scores
Understand industry and peer group expectations for business outcomes and IT outcomes for an increasingly mature (sophisticated) use of cloud in how organizations operationalize their internal IT assets and how they source capability on the public (provider-based) cloud
Understand the supporting processes, governance models, technologies, and skills to deliver cloud-based service delivery models to their internal customers
Identify gaps in overall cloud management capabilities based on the aforementioned criteria — or more broadly, vision, technology, people, and process
Cloud Maturity


text:
PATHS TO PUBLICATION: Michael A. Arnzen (RDSP)
PATHS TO PUBLICATION
For the next two weeks I'll be featuring authors from the cutting edge Raw Dog Screaming Press! Founded in August 2003, RDSP books are entertaining and thought-provoking and span the gamut from surreal and absurd to horror, sci-fi and fantasy.
The Gorelets Omnibus is a "collected works" volume of all my twisted horror poems from gorelets.com and elsewhere over the past decade. I'm proud of the book, which includes not only hundreds of pages of short-short horror poetry (reminiscent of the flash fiction from another of my Raw Dog books, 100 Jolts), but also -- available only in the special hardcover edition -- a variety of miscellaneous pieces that should inspire anyone who is interested in writing dark fiction or adding a little strangeness to their work.
For instance, there are five articles written by scholars from America and the UK, analyzing what makes the poems tick and what makes today's "new media" horror special. There is also a collection of "how to" articles on the crafting of horror poetry that I've published over the years. And -- perhaps best of all for the creative writers reading this -- this book includes the entire collection of my "Instigation" prompts from over the past ten years (including the impossible to find Instigation columns from Hellnotes magazine from years ago).
In other words, the hardcover edition of this book hides a short-course from me in writing horror. If you're a horror/mystery/dark fantasy author, I think this book will prompt your dark imagination in ways you might appreciate, even if you have no interest in poetry. (Though look out, future Poes and Lovecrafts -- this collection might change your mind!)
Go check it out at Raw Dog Screaming Press and sign up for my newsletter on gorelets.com for insight into what this book has to offer.
-Michael A. Arnzen
Over the past two decades, Michael A. Arnzen has won five Bram Stoker Awards for his avant horror fiction, his quirky dark poetry and his bizarro antics online at gorelets.com. His other titles from Raw Dog Screaming Press include a novel (Play Dead), a collection of flash fiction (100 Jolts: Shockingly Short Stories), a CD of musically-enhanced readings (Audiovile) and, forthcoming, a scholarly study of dread in pop culture (The Popular Uncanny). When he's not writing, Arnzen wears the mask of professor of English at Seton Hill University, home of the country's only MFA degree in Writing Popular Fiction. Get gored again and again at gorelets.com or join his social network at michaelarnzen.com.
Find Raw Dog Screaming Press online at these links:
LIVEJOURNAL - http://raw-dog.livejournal.com/profile
WEBSITE - http://www.rawdogscreaming.com
- http://www.facebook.com/pages/Raw-Dog-Screaming-Press/141926985837593
- https://twitter.com/RDSPress
Labels: 100 jolts, heidi ruby miller, horror, michael a. arnzen, paths to publication, poetry, raw dog screaming press, rdsp, the gorelets omnibus
Heidi Ruby Miller April 19, 2012 at 1:24 PM
What a treasure of your work over the years, Mike!
The book itself is beautiful.
Mike Arnzen April 19, 2012 at 5:05 PM
Thanks for hosting RDSP week! Love all these articles.
PATHS TO PUBLICATION: Gary Ryman
HEIDI'S PICK SIX: JC Cassels
HEIDI'S PICK SIX: Jodi McClure (GCP)
PATHS TO PUBLICATION: Cynthia Ravinski (GCP)
CAST YOUR CHARACTERS: Last Burn in Hell by John Ed...
CAST YOUR CHARACTERS: The Kyoto Man by D. Harlan W...
CAST YOUR CHARACTERS: Hellbender by Jason Jack Mil...
SFFS Snippet #1: "The Islands of Hope" in Sails an...
HEIDI'S PICK SIX: Paul A. Toth (RDSP)
HEIDI'S PICK SIX: Michael Gills (RDSP)
HEIDI'S PICK SIX: Mickey Z. (RDSP)
HEIDI'S PICK SIX: Larry Fondation (RDSP)
HEIDI'S PICK SIX: George Williams (RDSP)


SunPower is a leading solar technology and energy service provider that produces the most efficient solar cells and panels on the market today.
With the current and predicted growth in solar energy consumption, advances in Big Data analytics, and advances in their own smart systems and data-capturing potential, SunPower sees its future in the cloud.
SunPower now has a hosted environment that models their previous architecture but will let them transition to the cloud at a gradual pace that makes sense for their business.


What is nucleoside bypass therapy and could the treatment have helped Charlie Gard's mitochondrial depletion syndrome?
What is nucleoside bypass therapy and could the treatment have helped Charlie Gard's mitochondrial depletion syndrome?
THE world was touched by the case of Charlie Gard as his parents fought a relentless battle for permission to take him to the US for medical treatment.
Mitochondrial DNA depletion syndrome refers to a group of disorders that cause affected tissues to suffer from a significant drop in mitochondrial DNA.
These supply energy to the cells in the human body and when they fail the body's organs are damaged and shut down.
In Charlie's case energy was not getting to his muscles, kidneys and brain.
The 11-month-old inherited the rare RRM2B gene from his parents - who were unknowing carriers.
The vast majority of children with Mitochondrial DNA depletion syndrome do not survive.
Where it doesn't kill it causes permanent brain damage.
There is currently no cure but some treatments have shown a reduction in symptoms. Families of other children who've had the disease had differing views on the best course of action for Charlie.
What is nucleoside bypass therapy?
Charlie's parents were fighting for him to be taken to the US after a doctor in the States offered them nucleoside bypass therapy.
In theory it could have seen Charlie's mitochondrial DNA synthesise again by giving him the naturally occurring compounds his body isn't able to produce.
It is an experimental new treatment taken as an oral medication.
The drug has not even been tested on mice for Mitochondrial DNA depletion syndrome.
However, doctors say it has been tested on mice and patients with a different mitchondrial condition and has brought about "dramatic clinical improvements".
The family said they had seven specialist doctors - including one from England - supporting their calls for Charlie to be given the treatment.
A US expert said 18 people have been treated with nucleoside bypass therapy. But he added that none of them were in a condition as severe as Charlie's.
Which hospitals offered to treat Charlie?
New-York Presbyterian Hospital and Columbia University Irving Medical Center had said in a statement they would admit and assess Charlie for treatment.
Dr Michio Hirano is developing an experimental therapy used on at least one American patient with a less severe form of the disease.
The hospital said it would admit Charlie "provided that arrangements are made to safely transfer [Charlie] to our facility, legal hurdles are cleared, and we receive emergency approval from the US Food and Drug Administration (FDA) for an experimental treatment as appropriate."
Bambino Gesu hospital in Rome, Italy, also offered to take terminally-ill Charlie and had asked Great Ormond Street Hospital if he could be transferred.
Have other children been treated using nucleoside medication?
Five years ago, Art Jr appeared to be facing death when he became the first human to be given nucleoside medication.
Art said his son was given a "new lease of life" by the drug and can now stand with the help of a brace and say a small number of words.
He told Sun Online: "We were elated. It was like God had answered our prayers.
"Without the medication he would not be alive today, doctors said it and we have witnessed it.
"Before he would sleep all day. But we started to see a lot more energy.


Posted in Boxing Day Deals, Boxing Day Furniture Deals 2018, Boxing Day Furniture Sale, Boxing Day Sale, Furniture Sale and tagged Boxing Day Sale UK, Boxing Day Sale 2018, Boxing Day Deals, Boxing Day Sale, Boxing Day Furniture Deals, Boxing Day Furniture Sale by Tue, 04 Dec 2018 10:45:24 +0000.
Posted in Black Friday Deals, Black Friday Sales, Cyber Monday Furniture Sale, Furniture Sale and tagged Black Friday Furniture Offers, Black Friday Sale 2018, Black Friday, Black Friday Furniture Sale, Black Friday Furniture Deals, Black Friday Deals, Black Friday Sale by Fri, 23 Nov 2018 06:59:53 +0000.
Posted in Bedroom Furniture, Black Friday Sales, Cyber Monday Furniture Sale, Dining Room Furniture, Living Room Furniture and tagged Cyber Monday Sales, Cyber Monday Furniture Sale, Living furniture, Black Friday Furniture Deals, Black Friday Deals, Black Friday Sale, Bedroom Furniture by Mon, 22 Oct 2018 12:49:41 +0000.
Posted in Black Friday Sales, Dining Room Furniture, Italian Furniture, Living Room Furniture, Sofa and tagged sofa sale, sofas, Living furniture, Dining furniture, Black Friday Furniture Deals, Black Friday Deals, Black Friday Sale by Wed, 03 Oct 2018 13:05:27 +0000.
Posted in Bedroom Furniture and tagged Bedroom Furniture by Thu, 26 Jul 2018 04:39:52 +0000.
Posted in Garden Furniture by furnituredirectuk.
Posted in Dining Room Furniture by furnituredirectuk.
Posted in Bedroom Furniture, Beds & Mattresses, Dining Room Furniture, Home/Office Furniture, Italian Furniture, Kids Furniture, Living Room Furniture by furnituredirectuk.
How to select high quality mattresses?
Posted in Beds & Mattresses by furnituredirectuk.
Glass dining sets Vs. Wooden dining sets – Which is better?
Posted in Bedroom Furniture by furnituredirectuk.


text:
LPGA unveils 2016 schedule, with 33 events and $63 million in purses
By Tim Reynolds
Published on Friday, November 20, 2015
NAPLES, Fla. (AP) – Purses on the LPGA Tour will rise by $4 million in 2016, and officials are aiming for a second event in China.
The tour released next year's schedule on Friday, with 18 of the 33 official events held in the United States. Prize money will rise from $59.1 million to $63.1 million, a tournament has been added in Michigan and the LPGA will take a three-week break to accommodate the return of golf to the Olympics in August.
"Stability with growth," LPGA Commissioner Mike Whan said. "We've had some dramatic things we needed to address in the last six years ... and the numbers, I think, kind of speak for themselves."
In 2011, the LPGA had 23 official events and $40.5 million in purse money with 220 hours of television coverage. The TV hours will increase again to about 410 next year. The LPGA also announced Friday a partnership with the Oneida Tribe of Indians of Wisconsin for a tournament that will start in 2017 and offer a $2 million purse.
"It's an exciting time," Whan said.
The 2016 schedule starts with five tournaments in five different countries, matching the layout of the past two years. The season begins in the Bahamas in late January, and the first event in the United States is the Coates Golf Championship at Ocala, Florida. It ends Saturday, Feb. 6, and won't conflict with Super Bowl 50 the next day in Santa Clara, California.
The five majors are the ANA Inspiration ending April 3, the KPMG Women's PGA Championship ending June 12, the U.S. Women's Open ending July 10, the Women's British Open ending July 31, and The Evian Championship ending Sept. 18. The Olympic field will be set the day after the Women's Open, meaning a chance exists that the British Open champion might not be Rio-bound.
"The Olympics is making 2016 a little more crowded than a typical LPGA schedule in the middle of the summer," Whan said. "But it's a great problem to have when you have to get condensed because you're about to play in the Olympics for the first time in 100 years."
China was expected to host two events in 2015, though one was canceled about two weeks before the start. The LPGA plans to return for an event that ends Oct. 2. Whan said he's confident the second tournament – which is still listed as TBD on the schedule – will come together as planned.
Other nations on the 2016 schedule: Australia, Thailand, Singapore, Canada, Malaysia, South Korea, Taiwan and Mexico.
Next season ends again at the CME Group Tour Championship on Nov. 20 in Naples, Florida.
Here is the schedule:
Jan. 29-31
LPGA Tour: Pure Silk-Bahamas LPGA Classic
Paradise Island, Bahamas
Feb. 4-6
LPGA Tour: Coates Golf Championship
Saturday finish
Feb. 18-21
LPGA Tour: ISPS Handa Women's Australian Open
LPGA Tour: Honda LPGA Thailand
Chomburi, Thailand
Mar. 3-6
LPGA Tour: HSBC Women's Champions
Mar. 18-20
LPGA Tour: JTBC Founders Cup
LPGA Tour: Kia Classic
Carlsbad, Calif.
Mar. 31-Apr. 3
LPGA Tour: ANA Inspiration
Rancho Mirage, Calif.
Apr. 14-16
LPGA Tour: Lotte Championship
LPGA Tour: Swinging Skirts LPGA Classic
Apr. 29-May 1
LPGA Tour: Volunteers of America Texas Shootout
LPGA Tour: Yokohama Tire LPGA Classic
Prattville, Ala.
LPGA Tour: Kingsmill Championship
Williamsburg, Va.
LPGA Tour: Volvik Championship
Ann Arbor, Mich.
LPGA Tour: ShopRite LPGA Classic
Galloway, N.J.
LPGA Tour: KPMG Women's PGA Championship
Sammamish, Wash.
LPGA Tour: Meijer Classic
Belmont, Mich.
LPGA Tour: Walmart NW Arkansas Championship
Rogers, Ark.
LPGA Tour: Cambia Portland Classic
LPGA Tour: U.S. Women's Open
CordeValle, Calif.
LPGA Tour: Marathon Classic
Sylvania, Ohio
LPGA Tour: International Crown
LPGA Tour: Ricoh Women's British Open
Milton Keynes, England
Aug. 18-21
Olympics Women
LPGA Tour: Canadian Pacific Women's Open
Sept. 2-4
LPGA Tour: Manulife LPGA Classic
LPGA Tour: Evian Championship
Evian Les Bains, France
Oct. 7-9
LPGA Tour: Sime Darby LPGA Malaysia
Oct. 14-16
LPGA Tour: KEB-Hana Bank Championship
Incheon, Korea
LPGA Tour: Fubon LPGA Taiwan Championship
LPGA Tour: Blue Bay LPGA
Hainan Island, China
Nov. 4-6
LPGA Tour: Toto Japan Classic
LPGA Tour: Lorena Ochoa Invitational
LPGA Tour: CME Group Tour Championship
Naples, Fla.
Copyright (2015) Associated Press. All rights reserved. This material may not be published, broadcast, rewritten, or redistributed. This article was written by Tim Reynolds from The Associated Press and was legally licensed through the NewsCred publisher network.


Until 1975-1980 cruises offered shuffleboard deck chairs drinks with umbrellas and little else for a few hundred passengers. After 1980 they offered increasing amenities. As of 2010 city-sized ships have dozens of amenities.
There is often a central galley responsible for serving all major restaurants aboard the ship though specialty restaurants may have their own separate galleys.
By Adalrik Engel. type of ships. Published at Tuesday, April 23rd, 2019 17:57:11 PM.


text:
Comparison of back versus thigh donor sites in pediatric patients with burns
David G Greenhalgh, P. P. Barthel, G. D. Warden
To substantiate an observation that donor sites that are harvested from the back scar less than those that are harvested from thighs, donor sites from both areas were evaluated for the extent of scarring. For donor sites that were harvested more that 1 year before evaluation, no significant improvements were observed. Back donor sites that were evaluated less than or equal to 1 year after harvest had significant improvements in height, color, edema, suppleness, and cosmetic appearance than those harvested from thighs. Thirty-two patients had both back and thigh donor sites harvested at the same time. Back donor sites had significant improvement in scar height, color, and edema. Cosmetic improvements were also observed. Significantly more patients preferred the back donor site over that of the thigh. The back is the preferred donor site for skin grafts in the pediatric burn population.
Journal of Burn Care and Rehabilitation
Dive into the research topics of 'Comparison of back versus thigh donor sites in pediatric patients with burns'. Together they form a unique fingerprint.
Burns Medicine & Life Sciences 100%
Back Medicine & Life Sciences 96%
Thigh Medicine & Life Sciences 79%
Tissue Donors Medicine & Life Sciences 76%
Pediatrics Medicine & Life Sciences 62%
Cicatrix Medicine & Life Sciences 31%
Color Medicine & Life Sciences 20%
Edema Medicine & Life Sciences 19%
Greenhalgh, D. G., Barthel, P. P., & Warden, G. D. (1993). Comparison of back versus thigh donor sites in pediatric patients with burns. Journal of Burn Care and Rehabilitation, 14(1), 21-25.
Comparison of back versus thigh donor sites in pediatric patients with burns. / Greenhalgh, David G; Barthel, P. P.; Warden, G. D.
In: Journal of Burn Care and Rehabilitation, Vol. 14, No. 1, 1993, p. 21-25.
Greenhalgh, DG, Barthel, PP & Warden, GD 1993, 'Comparison of back versus thigh donor sites in pediatric patients with burns', Journal of Burn Care and Rehabilitation, vol. 14, no. 1, pp. 21-25.
Greenhalgh DG, Barthel PP, Warden GD. Comparison of back versus thigh donor sites in pediatric patients with burns. Journal of Burn Care and Rehabilitation. 1993;14(1):21-25.
Greenhalgh, David G ; Barthel, P. P. ; Warden, G. D. / Comparison of back versus thigh donor sites in pediatric patients with burns. In: Journal of Burn Care and Rehabilitation. 1993 ; Vol. 14, No. 1. pp. 21-25.
@article{fe578115ac2a49dc8ea5e958d64aa8d6,
title = "Comparison of back versus thigh donor sites in pediatric patients with burns",
abstract = "To substantiate an observation that donor sites that are harvested from the back scar less than those that are harvested from thighs, donor sites from both areas were evaluated for the extent of scarring. For donor sites that were harvested more that 1 year before evaluation, no significant improvements were observed. Back donor sites that were evaluated less than or equal to 1 year after harvest had significant improvements in height, color, edema, suppleness, and cosmetic appearance than those harvested from thighs. Thirty-two patients had both back and thigh donor sites harvested at the same time. Back donor sites had significant improvement in scar height, color, and edema. Cosmetic improvements were also observed. Significantly more patients preferred the back donor site over that of the thigh. The back is the preferred donor site for skin grafts in the pediatric burn population.",
author = "Greenhalgh, {David G} and Barthel, {P. P.} and Warden, {G. D.}",
journal = "Journal of Burn Care and Research",
T1 - Comparison of back versus thigh donor sites in pediatric patients with burns
AU - Greenhalgh, David G
AU - Barthel, P. P.
AU - Warden, G. D.
N2 - To substantiate an observation that donor sites that are harvested from the back scar less than those that are harvested from thighs, donor sites from both areas were evaluated for the extent of scarring. For donor sites that were harvested more that 1 year before evaluation, no significant improvements were observed. Back donor sites that were evaluated less than or equal to 1 year after harvest had significant improvements in height, color, edema, suppleness, and cosmetic appearance than those harvested from thighs. Thirty-two patients had both back and thigh donor sites harvested at the same time. Back donor sites had significant improvement in scar height, color, and edema. Cosmetic improvements were also observed. Significantly more patients preferred the back donor site over that of the thigh. The back is the preferred donor site for skin grafts in the pediatric burn population.
AB - To substantiate an observation that donor sites that are harvested from the back scar less than those that are harvested from thighs, donor sites from both areas were evaluated for the extent of scarring. For donor sites that were harvested more that 1 year before evaluation, no significant improvements were observed. Back donor sites that were evaluated less than or equal to 1 year after harvest had significant improvements in height, color, edema, suppleness, and cosmetic appearance than those harvested from thighs. Thirty-two patients had both back and thigh donor sites harvested at the same time. Back donor sites had significant improvement in scar height, color, and edema. Cosmetic improvements were also observed. Significantly more patients preferred the back donor site over that of the thigh. The back is the preferred donor site for skin grafts in the pediatric burn population.
JO - Journal of Burn Care and Research
JF - Journal of Burn Care and Research


Share screens for productive meetings with GoToMeeting.
Kiss the days of tedious meetings goodbye. With screen sharing software, you can ensure that all attendees are on the same page, literally.
GoToMeeting enables us to work remotely and not to be tied to one office. Without flexible ways of working and robust collaboration tools we would simply not be able to operate. It's integral to our business and has helped us to build a global network of customers and employees.


Here, ten ways to lock down a less pricey flight.
True story: The best airfare deals almost always get announced on social media first (cough, cough JetBlue).
Seven weeks is the sweet spot when it comes to locking down the best rate, according to airfare experts. If you book too early--or worse, too late--you face a greater risk of expensive fares.
Planes typically have the most seat availability on Tuesdays and Wednesdays. If your schedule is flexible, look for flights on those days since airlines are more likely to drop ticket prices to ensure a sold-out trip.
Just like with hotels, the best airline deals sometimes come straight from the airlines themselves. It's OK to cast a wide net and scan Orbitz for deals, but don't be surprised if you find the best rate on the airline website.
It pays to be an insider. Plus, the extra email clutter is worth it if it means scoring a surprise 20 (or 50!) percent off promo code.
Whenever airlines announce a new destination (like direct flights from Newark to Venice), they usually offer low prices to help promote it. How do you stay in the loop? By signing up for the airline newsletters, of course.
Google Flights makes it easy to play around with different currencies--say, paying with pesos instead of dollars--a strategy that tricks the airline system into thinking you're traveling from a different locale. This could save you anywhere from $10 to $50. Go here for more details.
You know, the third-party kind. Some airlines track how many times you've searched a particular flight route, then use that info to jack up the price. You've been warned.
Sure, you wanted to fly direct to LAX, but what if flying to Long Beach could save you $100? Yes, it's about an hour outside the city, but depending on the cost of local transportation, it could end up being cost-effective in the long run.
A lot can change in a day. If you spot a better deal, remember that most airlines give you 24 hours to cancel (or rebook at a cheaper rate).


text:
» Evidence Based Policymaking in Europe Summit 2021
Social innovation for climate change adaptation & mitigation
Evidence Based Policymaking in Europe Summit 2021
Join the IntelComp project and others discussing cloud computing, big data and AI contributing to public policy making
Datum: 9. - 10. Dezember 2021
The convergence of Cloud, Big Data and Artificial Intelligence has already resulted in major transformation across Government services, yet the process of policy making itself is often left behind.
Join European projects Policy Cloud, Decido, AI4PublicPolicy, DUET and Intelcomp at the virtual "Evidence Based Policy Making in Europe event" to explore major challenges, trends and opportunities to improve public sector decision making that will deliver healthier, happier places to live and work.
Digital technologies have changed the world. Today people expect faster, seamless, on-demand services from their providers, and Government is no exception. For effective urban operations which make life easier for residents, workers and visitors, Public Sector decision making needs to become more agile, breaking down data silos to combine day-to-day tactical decisions with longer term policies and strategies. Disruptive technologies such as Digital Twins, Artificial Intelligence and High Performance Computing (HPC) unlock new opportunities for sustainable decision making through visualisations, simulations and predictions that enhance transparency, increase public support and involvement, and optimise resources.
To support this transformation, the Policy Cloud, Decido, AI4PublicPolicy, DUET and Intelcomp pan-European projects and initiatives dedicated to using the cloud for data-driven policy, have joined forces to host Evidence Based Policy Making in Europe, a premier conference for government that focuses purely on data and tools for decision making. Together with leading change agents from the European Commission and Local Government, the event will explore the new decision-making ecosystems being built by cities and administrations, including the use cases being adopted, and the innovative data and tools being adopted for modern policy making.
Taking place virtually on the 9th and 10th of December, 2021, here's what to expect:
Use Cases: We've asked our speakers to bring real-life examples of how they are transforming the traditionally slow deliberative policy making process in the fields of health, climate change and mobility, to one that is more agile and responsive. Hear directly from cities themselves on how they are developing better evidence-based policies which adapt as new data comes to light, and which are trusted and contributed to by relevant stakeholders who feel engaged in the process.
Tools: See how city managers and policy makers are co-creating data-driven decision-making ecosystems with cutting edge tools that enable them to visualise, analyse and even predict the complex impact of decisions across multiple domains, time and space. Be hands-on with AI, big data and digital twin demos and be inspired by what you could potentially achieve for your administration
Strategies: Learn how to manage legal, ethical and standards challenges and constraints in our increasingly digital world. From best practices to principles and legislation our speakers will help you understand and navigate the complex environment in pragmatic ways that will get you started on your journey.
Attendance is open to all. Join us as we forge the future of modern policy making.
9th December Day 1: Evidence Based Policy Cases - from data to decision making
Given the complexity of our societies and the rapid generation of data, how can public authorities transform the usually slow, deliberative policy making process to one that is more agile and responsive, which develops better evidence-based policies which adapt as new data comes to light, and which are trusted, understood and contributed to by relevant stakeholders.
11:30 - 11:45 Evidence-Based Policy Cases - Pilot Studies
11:45 - 12:15 Coffee break
12:15 - 13:30 Track 1 - Health & Social Track 2 - Climate Change Track 3 - Mobility
13:30 - 14:00 Recommendations from the 3 tracks
15:00 - 17:00 Innovation workshop with adopters
10th December Day 2: Innovative Tools for Evidence-Based Policy - from digital disruption to digital adoption
The convergence of Cloud, Big Data and AI has already caused considerable transformation across Government. With citizens demanding better experiences as their expectations shift towards quick, seamless and personalised services, now is the time for Public Sector decision makers to embrace digital disruption and new innovative technologies to make more sustainable policy based on real-time information, predicted impact and citizen input.
11:30 - 11:45 Innovative Tools for Evidence Based Policy - Discussions
A New Era of Collaboration, Co-creation & Consultation with Citizens & Stakeholders
Forging the Future with Policy Impact Analysis & Prediction
Managing Legal, Ethical & Standards Challenges and Constraints in a Digital World
13:30 - 14:00 Wrap -Up
Veranstaltungsort: online
Webseite: https://intelcomp.eu/events/ev ... ummit-2021
Projekt: A Competitive Intelligence Cloud/HPC Platform for AI-based STI Policy Making
Tags: artificial intelligence, policy analysis, research policy, STI system


Pak Mail is your leader in freight & logistic solutions for commercial and industrial businesses. Our expansive carrier network enables us to find the right carrier for any freight shipment, and our long-time partnerships ensure our clients receive reliable and dedicated service, whether you have a one-time shipment or an ongoing freight need.
Commercial and industrial shipping requires safe and timely arrival, and our Pak Mail logistics professionals are here to get your freight where it needs to go safely and on-time. With Pak Mail, you are able to take advantage of our competitive rates acquired over our 30 years in business fostering a dedicated and reliable network of carrier partners.
The Pak Mail logistics team is here to help you with all of your commercial and industrial freight shipping needs. Contact a Freight Specialist Today.


The Sureflap Cat Flap Mounting Adaptor is useful when installing the SureFlap Microchip Cat Flap or DualScan Microchip Cat Flap into glass, walls and certain door types. This accessory allows mounting in holes ranging from 210mm to 260mm (8½" to 10¼") in diameter. The total diameter of the mounting adaptor is 285mm (11⅛").
Each cat flap mounting adaptor includes two parts, one for the interior and one for the exterior of your home. Available in white or brown.
When mounting the SureFlap Microchip Cat Flap in a glass pane, your glazier will generally cut a circular hole, leaving open areas around the corners of your cat flap. The optimum hole size for mounting in glass is 212mm in diameter. The cat flap mounting adaptor can cover hole sizes ranging from 210mm to 260mm in diameter. The SureFlap Cat Flap Mounting Adaptor covers these open areas, preventing loss of heat from your home and providing a clean, neat finish to your installation. Learn how to install the SureFlap Microchip Cat Flap in glass using the Installation Guides.
The cat flap mounting adaptor overcomes the inherent problems of installing a cat flap directly into a wall. This installation technique avoids the use of messy adhesives, which can make removing your cat flap at a later date a potentially difficult exercise. It also means that you do not have to drill through any part of your cat flap during installation. Learn how to install the SureFlap Microchip Cat Flap in a wall using the Installation Guides.
The SureFlap Microchip Cat Flap has been designed as a drop-in replacement for most existing cat flaps. However, in some instances you may find that the hole left by your previous cat flap is larger than the hole required for the SureFlap cat flap. The SureFlap Cat Flap Mounting Adaptor provides a neat way to cover such a hole.


text:
Vishal Bhardwaj: Aamir Khan the only daring actor
by Abhik Rai
Taapsee Pannu: I've been crazily adventurous with my life
Sanjay Khan: You can smell hypocrisy from a mile away...
Sanjay Khan: You can smell hypocrisy from a mile away in industry today
Veteran actor Sanjay Khan says he has enjoyed several long-lasting friendships in Bollywood but believes the equation among the current crop of actors is so superficial that they are ready to "backbite" anytime. Khan says these things existed in the industry in his heyday but there was elegance and class.
"The value of friendships among the current reigning lot pains me. It is superficial. It's just to show courtesy. The extra hugs and the taps on the back, you can smell hypocrisy from one mile away, that they don't have anything in common," Khan said.
In This Image: Sanjay khan
"The moment they turn around, they backbite. This is a downside of the film industry. In those days we could say it was like this too but it had elegance, some class, respect. Now there is naked aggression," he adds. Khan says the reason for the downfall of quality relationships between people in the industry could be "material gain." "In those days, the parity of money was more or less the same. Today with changing times and too much of money and politics in films, (there is a) dog-eat-dog mentality."
Some of Khan's closest friendships and other important events of his life find a mention in his autobiography "The Best Mistakes of my Life", published by Penguin Random House India.
The "Dosti" actor's life changed after a major fire accident which took place in 1989 on the sets of his TV show "The Sword of Tipu Sultan", in which he played the title role, alongside directing and producing. Fifty-two people died in the fire. Khan, 78, recalls how Dharmendra came to visit him in the hospital and was "crying like a boy."
"Two months after I came back from coma, I saw a stream of VIPs from Delhi and Dharmendra.
Those people were watching me with pathetic looks, I was telling them I'll meet them soon but they must've thought I am a goner and were expressing sympathies with me. But Dharmendra was crying like a boy. He kept on saying, 'My friend, I'm with you.' That was the friendship I shared with him. I shared an extremely long and close friendship with Raj Kapoor and Sanjeev Kumar among others." Khan still remembers the day of the mishap.
"The whole set was consumed by fire. Smoke and flames had engulfed me. I was hit on my head with something like a cannonball, which later I was told was a tin of paint I fell and was taken to the hospital.
I had 65 percent third-degree burns, 52 of my crew lost their lives. I've carried this pain for a long time. Only seven months after the accident, I was informed about it." The actor says he was told by the doctors that he could never work again but his will kept him going. "My survival itself was rare – trauma with third-degree burns, 73 surgeries, I was called 'The Miracle Man'. But I knew I must work, otherwise, I would die."
Posted by Abhik Rai
LifestyleHalloween in India, Why is Halloween getting popular in India