Why is it that as a new religion, people in the pagan community feel the need to make it seem like we've been around for a few hundred years? I mean, I know that Gardener (1965!) based the neo-paganism off of some research from older spiritualities, but the fact remains that we've only been around a little over 35 years.
1) Between the time that Christianity started converting people and the introduction of neo-paganism, there was a major date system change. The calander was modified to make it fit more closely to the astrological. This affords some date-shifting.
2) Most of the holidays celebrated by neo-pagans today were created LONG after Christianity had established itself.
3) Eostar, even IF it did exist before Easter was NOT stolen. The dating of Easter is based off of the lunar calander and falls on the first Sunday after the first full moon after the Spring Equinox. By the way, this holiday is also closely linked to Passover, a Jewish Holiday that has been around a hell of a lot longer than most.
I'm a pagan. I like the fact that people are proud of our diverse spirituality. I wish that we could get away from our flaky media image, but this is never going to happen as long as people keep claiming they're Napolean re-incarnated, they can psychically blast people from across the world, or that our religion is the oldest one in existence, 'cause guess what, it's not!
Sorry! Did I read that right! Paganism is new!!!!
I am an old Pagan in every sense. Firstly I am old myself, being well into my second half century, and have been a pagan my entire life. Secondly, my family have been mostly pagan for generations. Many of our family practices have been passed down for centuries. If you want to trace back pagan families in England you simply go and look at the local churchyard. The pagans are the ones whose gravestones are outside the boundary wall on unconsecrated ground.
Paganism predated christianity by a few thousand years. The Christians moved the events on their calendar to match those of the pagans because they could not find a way to stop the pagan celebrations. The actual date of say Easter, may have changed. However, as an Old Pagan, I follow the moon and tides and my celebration is not exactly on the Christian Easter, merely sometimes close to it. Other events such as the Solstice or equinox have existed and been celebrated since the dawn of (pagan) man.
Your problem is that you are thinking in terms of America, where there was a long break in the pagan tradition. (I'm excluding native Americans here, no offence intended). Paganism has existed in Europe all through the Christian period. Passed on through families and by word of mouth over the generations. Yes, it has changed and evolved and is stronger for that. Few of those families who carried the religion yet feel safe enough to come out into the open, much less write books about it!
Do not think that all you read is all there is. Try not to insult us old folk with your ignorance.
On the other hand, as Mythellin points out, "Do not think that all you read is all there is." There is a lot of poorly researched material used as source material by other others that blow the connection between the holidays waaaaaaaaaay out of proportion. There are connections, but they aren't as important (or at least not in the ways usually implied) as these books claim.
The other point you're making (about the age of Paganism) also has some validity. Most of what is done is not the pagan faith of our forebears. We have adapted from many sources, often without there being a direct connection. Some connections do still exist, albeit shaped and altered by having to go underground. We're a living religion, we live with change.
Finally, Mythellin, your comment "Your problem is that you are thinking in terms of America, where there was a long break in the pagan tradition." What makes you think some families didn't (by choice or otherwise) end up over here too....?
Quite right Debbrah! I made the assumption that all immigrants to the "New World" were Christians (such as the Pilgrim Fathers). Obviously some of the millions who moved there must have been Pagan. There is a very strong pagan following in Eire and many Irish families moved to the states.
Incidentaly, there is also a Welsh Druid community in Argentina with an unbroken pagan history. Just goes to show how strong and enduring paganism is!
I wanna vote! Debbrah makes a good point, lol.
Anyhow, Ostara (the tradition of the eggs) actually, from what I've read, it is a hell of a lot older, sorry for the language, I figured you'd be okay cuz that's the terms you used.
Most of the pagan holidays are a lot older, even though wiccans gripe about them, they have somewhat of a right to because they're still not recognized as being actual pagan holidays when they very well should be. Of course, most of us just worship on the holidays under wraps so to speak so why should we care at all?
They don't even need to know why we may decide to exchange presents on the winter solstice instead of "Christmas Day". Why even worry about it any more? I've fought this battle before... no one wins because christians won't accept any kind of proof that it was pagan first.
Also, if Ostara wasn't stolen and made Easter then why do they still go on egg hunts? Hello, if this were a real christian holiday they go inside, pray for some Saint, sing and it'd be over with!
Easter Eggs represent fertility, the rebirth of the land, of life itself, and many many Europeans cultures practiced colouring them in honour of the Goddess. My heritage (in this walk) is Ukranian, and my ancestors turned this into an incredible art form, blowing out the insides and "painting" Goddess symbols on the shell using beeswax and dyes. Over the centuries of Christian influence, the meaning of the symbols changed to Christianized values, but the symbols did not! Chocolate bunnies. Ah yes, show me where in the Bible (any one's version!) where it talks about rabbits! Chocolate bunnies are the remnant of honouring the hare, sacred to the Goddess, one of Her "shamanic" animas as a Goddess of fertility. In Ireland, the hare was (and is) recognized as such, and the people will not eat rabbit (it'd be like eating your grandmother!) And while I'm on the subject, hot cross buns are not about the cross on Calvary, but rather are the remnant of sacred cakes left at the crossroads to honour Dianna, Hecate, etc.!
Okay, so people have brought up a lot of good comments on the validity of reading material written by sources. I have a problem with the material posted on paganlibrary that states that Christian holidays are only a reflection or an attempt to convert early pagans. The majority of people who have read my post have a problem with the fact that I don't believe these statments carte blanche.
People have gone so far with protecting their rights to practice the religion they choose that some have started attacking other religions as false, inferior, or downright stupid. All of these are a form of intolerance that I left my particular old faith to get away from. As I said in my first post, I love paganism, I currently practice a form of neopaganism. I'm just trying to look at it through the lense of reality and sensibility.
Also, to quote Robin Williams, you don't really want parents spreading jam on the front lawn going "c'mon kids, let's go find Jesus!" and getting children to eat cream-filled crosses.
A lot of the problem, I think, is that "pagan" is a catchall term for any non-ChristianJewishMuslimBuddhist (etc.) form of worship. So is believing in the old Norse or Greek gods pagan? Sure, why not. Is worshipping a Goddess pagan? Frequently. So yeah, there may be common links between the past druids, pagans et al. and today's pagans - but Judaism and Catholocism are not entirely the same religion, and Catholocism isn't entirely like any Protestant Christian religion.
the pagan thing I said was according Mythellin's post about pagan pilgrims (read above) and I'm not saying all non-christian are pagans.
I don't know if it's your case, but in catholic churches there are only a nativity set, I have not seen any tree, have you?
Well, I have several things to say here.
First off, neopagans have a right to complain as the traditions that they practice derive from traditions practiced in much older religions. The traditions have not changed. Being they are under a different religion this makes no difference/they are still practicing it in representation of the same thing. Therefore, they still have every right to site the way christians use the holidays in misrepresentation.
Secondly, you must study the christian religion a little bit to understand the argument which has come up. The Protestant churches started springing up AFTER the Roman Catholic Church, which set the rules for all other churches. Slightly after 150 AD, the Roman Catholic Church, under an attempt to convert more pagans which already existed within their rule, began converting holidays. When the protestant churches sprung up at a later time they had already adopted these "Christianized" holidays. So being protestant does not automatically make you in the right. You can't go and try to say that Jesus was actually born anytime in December. Fact has proven that WISE men don't walk in the dark in the freezing cold!
musesShadow, to answer your question, I practice Druidism. I believe in reincarnation, I practice the art of magick in the act of making things happen.
musesShadow wrote: I have a problem with the material posted on paganlibrary that states that Christian holidays are only a reflection or an attempt to convert early pagans.
Okay. I'll agree. No question that the holidays also serve a purpose. There are basic needs every religion has got to attempt to meet. Many of the Christian holidays are adaptations because they were what the people knew when they converted to Christianity. They did the job. Some of the reasoning was, however, that if the didn't do it, the locals would remain pagan. You can do the research in church documents to prove it. Popes even endorsed it.
The fact of the matter is that what the majority of people practice today is a form of neopaganism which HAS only been around little more than 35 years, even if they were BASED ON older traditions (see first post).
For those who claim to follow older forms of paganism, I'd like to ask which practices in specific they practice that are specifically pagan.
Honestly? Everything and nothing. There aren't any of the basic ideas that can't be found in religions like christianity, which leads back to there isn't anything in christianity that can't be found in paganism (but we can claim to be older, or at least be connected to something older.... .) What is special is the way the pieces are put together. Trying to explain that, however, would make for a very long post, and a lot of it I don't think I would be able to try to post because it doesn't translate well to words. It's the stuff the traditions are as opposed to what they claim. It's the little details that cause and are the cause of a different mindset.
Like I said, there isn't anything you can't find in paganism that you can find elsewhere. We have fanatics and fundies too. I suggest you research some of the source material before you make too many judgements about what is reasonable. Read some of the writtings and church documents from the first couple centuries A.D and the Middle Ages. You seem very dedicated to the idea that there can't be anything to the idea. Why is that so certain? Why not some very important truths that are exaggerated?
You have to remember something: Paganism and Wicca are two separate things. Wicca is the young religion you're describing. The first piece of Wiccan literature to ever come out is Gerald Gardner's "Witchcraft Today" (1954). On the other hand, Paganism is a generic term for just about anything not in the same family of religions as Christianity, Judaism, and Islam, although Paganism is generally meant to include any polytheistic religion. To that effect, I don't quite consider Wicca a Pagan religion, but rather its own path. Most Pagan paths predate Christianity, even predate Judaism. Wicca has at the most a 50-year history.
Although Passover is a valid Jewish holiday, Christianity never adopted Passover. Christianity had no holidays until force-conversion was conducted in Europe. The very holiday of Easter you're describing, at least in the middle European sense, was stolen from those who worshipped the Anglo-Saxon Spring Goddess Ostara. In other cultures, such as Greek, Easter was taken from Spring Equinox. In the Norse countries, Easter replaced Springblot, in which the Goddess Idunn was honored. Christianity adopted these holidays, among others, to aid in the conversion of native peoples throughout Europe.
Although you have valid points, muses, you suffer under one too many misconceptions. Remember, there never was and never will be a unifying religion known as Paganism. Paganism is too general of a term in the first place to be any specific religion. To that effect, remember that the terms "Paganism" and "Wicca" aren't interchangeable. They mean distinct and separate things.
Sorry to harp on this, crone, but the Easter Eggs were actually in honor of whoever the local Fertility Goddess was, not the Wiccan Goddess. There was no Wiccan Goddess until Gerald Gardner and Doreen Valiente culminated various Pagan and non-Pagan sources into Wicca in the mid-20th Century. I hate to keep harping on this, but why can't Wiccans accept the fact that there are Gods and Goddesses out there who are separate and distinct from their God and Goddess?


Justin Lewellen is a Director of Investor Relations of Humphreys Capital. In this role, he shares the story behind our team and communicates the strategy and performance of our investment platform while initiating relationships with long-term partners.
Justin brings over a decade of finance, investor relations, and capital markets experience to Humphreys Capital. He began his career at Chesapeake Energy in 2006 and later served in various strategic finance and valuation-based disciplines at SandRidge Energy before becoming their Director of Investor Relations. In this role Justin was responsible for all corporate messaging and management of the firm's institutional investor base. In early 2018, Justin joined the executive management team of Lagoon Water Solutions, an Oklahoma based oilfield services and midstream water logistics company. In his role as Chief Financial Officer, he oversaw all financial, accounting, risk management and strategic marketing efforts that culminated in a $500 million private equity commitment. Justin joined Humphreys Capital in February 2019.
Justin earned a Master of Business Administration with an emphasis in Finance and Risk Management from the University of Oklahoma in 2015 and a Bachelor of Business Administration in Economics from Oklahoma State University in 2006.
He and his wife, Amanda, have two children and reside in Oklahoma City where they both serve as teaching leaders in Bible Study Fellowship.


text:
\section{Introduction}

Reinforcement learning is a computational approach to learning via interaction.
An algorithmic agent is tasked with determining a policy that yields a large cumulative reward.
Generally, the framework under which this agent learns its policy falls into one of two groups: model-free reinforcement learning or model-based reinforcement learning.
In model-free reinforcement learning, the agent acts in ignorance of any explicit understanding of the dynamics of the environment, relying solely on its state to make decisions.
In contrast, in model-based reinforcement learning, the agent possesses a \textit{model} of how its actions affect the future.
The agent uses this model to reason about the implications of its decisions and \textit{plan} its behavior.

The model-based approach to reinforcement learning offers significant advantages in two regimes.
The first is domains in which acquiring experience is expensive.
Model-based methods can leverage planning to do policy improvement without requiring further samples from the environment.
This is important both in the traditional Markov decision process setting, where sample efficiency is often an important performance metric,
and also in a more general pursuit of artificial intelligence, where an agent may need to quickly adapt to new goals.
Second is the regime in which capacity for function approximation is limited and the optimal value function and policy cannot be represented.
In such cases, agents that plan at decision-time can construct temporary local value estimates whose accuracy exceed the limits imposed by capacity restriction \cite{silver2008sample}.
These agents are thereby able to achieve policies superior to those of similarly limited model-free agents.

Far from being special cases, sample-sensitive, limited-capacity settings are typical of difficult problems in reinforcement learning.
It is therefore not surprising that many of the most prominent success stories of reinforcement learning are model-based.
In the Arcade Learning Environment (ALE) \cite{bellemare2013arcade}, algorithms that distribute training across many copies of an exact model of the environment have been shown to massively outperform algorithms limited to a single instance of the environment \cite{r2d2}.
And in Chess, Shogi, Go, and Poker, superhuman performance can be reached by means of decision-time planning on exact transition models \cite{silver2018general,deepstack,libratus}.

However, the premise of these successes is not the same as that of the classical reinforcement learning problem.
Rather than being asked to learn a model from interactions with a black box environment, these agents are provided an exact model of the dynamics of the environment.
While the latter is in itself an important problem setting, the former is more central to the pursuit of broadly intelligent agents.

Unfortunately, learning a useful model from interactions has proven difficult.
While there are some examples of success in domains with smooth dynamics \cite{pilco,planet}, learning an accurate model in more complex environments, such as the ALE, remains difficult.
In a pedagogical survey of the ALE, \citeauthor{marlos} (\citeyear{marlos}) state ``So far, there has been no clear demonstration of successful planning with a learned model in the ALE.''
Until recently \cite{muzero}, basic non-parametric models that replay observed experiences \cite{per} remained convincingly superior to state-of-the-art parametric models \cite{van2019use}.

Some of the difficulty of increasing performance with a learned model arises from the multifold nature of the issue.
Learning a useful model is itself a challenge.
But even once a model is learned, it is not clear when and how an imperfect model is best used.
The use of an imperfect model can be catastrophic to progress if the model is incorrectly trusted by the agent \cite{talvitie2017self, jafferjee2020hallucinating}.

This work concerns itself with how to effectively use an imperfect model.
We discuss planning methods that only use the model where it makes accurate predictions.
Such techniques should allow the agent to plan in regions of the state space where the model is helpful, but refrain from using the model when it would be damaging.
We refer to this idea as \textit{selective planning}.

There are two interrelated problems involved in selective planning: determining when the model is and is not accurate, and devising a planning algorithm which uses that information to plan selectively.

We formulate the first problem as that of predictive uncertainty estimation and consider three sources of predictive uncertainty---aleatoric uncertainty, parameter uncertainty, and model inadequacy---emphasizing the relevance of model inadequacy for selective planning under limited-capacity.
We demonstrate that the learned input-dependent variance \cite{nix1994estimating} can reveal the presence of predictive uncertainty that is not captured by standard tools for quantifying parameter uncertainty.

We address the second problem by empirically investigating selective planning in the context of model-based value expansion (MVE), a planning algorithm that uses a learned model to construct multi-step TD targets \cite{feinberg2018model}.
The results show that MVE can fail when the model is subject to capacity constraints.
In contrast, we find that selective MVE, an instance of selective planning that weights the multi-step TD targets according to the uncertainty in the model's predictions, can perform sample-efficient learning even with an imperfect model that otherwise leads to planning failures.

\section{Background}

This section provides background on model-based reinforcement learning, sources of predictive uncertainty, and previous work exploiting estimates of parameter uncertainty for model-based reinforcement learning.

\subsection{Model-Based Reinforcement Learning}
Reinforcement learning problems are typically formulated as a finite Markov decision processes (MDPs).
An MDP is defined by a tuple $(\mathcal{S}, \mathcal{A}, r, p)$, where $\mathcal{S}$ is the set of states, $\mathcal{A}$ is the set of actions, $r \colon \mathcal{S} \times \mathcal{A} \times \mathcal{S} \to \mathbb{R}$ is the reward function, and $p \colon (s_t, a_t, s_{t+1}) \mapsto P(S_{t+1}{=}s_{t+1} \mid S_t{=}s_t, A_t{=}a_t)$ is the dynamics function.
At each time-step $t$, the environment is in some \textit{state} $s_t \in \mathcal{S}$, the agent executes an \textit{action} $a_t \in \mathcal{A}$, and the environment transitions to state $s_{t+1} \in \mathcal{S}$ and emits a reward $r_{t+1} = r(s_t, a_t, s_{t+1})$.
The agent acts according to a \textit{policy} $\pi: \mathcal{S} \to \Delta(\mathcal{A})$, which maps states to probabilities of selecting each possible action (we use $\Delta(X)$ to denote the simplex on $X$).
The agent may maintain this policy explicitly or derive it from a value function $v \colon \mathcal{S} \to \mathbb{R}$ or an action-value function $q \colon \mathcal{S} \times \mathcal{A} \to \mathbb{R}$, which predict an expected discounted cumulative reward, given the state and state-action pair, respectively.
The agent's goal is to use its experience to learn a policy that maximizes expected discounted cumulative reward.

In model-based reinforcement learning, the agent leverages a model capturing some aspects of the dynamics of the environment.
This work regards the problem setting in which the agent must learn this model from its experience, rather than being endowed with it a priori.
In particular, the experiments in this work investigate learning models of the form $m \colon \mathcal{S} \times \mathcal{A} \to \mathbb{R}^{k} \supset \mathcal{S}$ (we assume $\mathcal{S}$ is embedded in the standard $k$-dimensional Euclidean space for some positive integer $k$).
Such models deterministically predict the expected next state from the current state and action.
While there is nothing that constrains their predictions to the state space and they are unable to express non-deterministic transitions, models of this form can still offer useful information.

Dyna \cite{sutton1991dyna} is an approach to model-based reinforcement learning that combines learning from real experience and experience simulated from a learned model.
The characterizing feature of Dyna-style planning is that updates made to the value function and policy do not distinguish between real and simulated experience.
In this work, we investigate the idea of selective Dyna-style planning.
An effective selective planning mechanism should focus on states and actions for which the model makes accurate predictions.

\subsection{Sources of Predictive Uncertainty}

Predictive uncertainty, the aggregate uncertainty in a prediction, arises from {\em aleatoric uncertainty} (due to randomness intrinsic to the environment), {\em parameter uncertainty} (due to uncertainty about which set of parameters generated the data), and model inadequacy, among other sources.
This section discusses the situations in which these sources of uncertainty appear in the context of model-based reinforcement learning.

\noindent\textbf{Aleatoric Uncertainty:}
In reinforcement learning, aleatoric uncertainty comes from the dynamics function $p$.
If the dynamics function induces non-deterministic transitions (those which occur with probability greater than zero but less than one), the agent cannot be certain what transition will occur.
Aleatoric uncertainty is irreducible in the sense that it cannot be resolved by collecting more samples or increasing the complexity of the model.

\noindent\textbf{Parameter Uncertainty:}
Parameter uncertainty is the uncertainty over the values of the parameters, given a parametric hypothesis class and the available data.
In model-based reinforcement learning, this is a result of the finite dataset used to train the model.
This dataset will not contain a transition for every state and action.
And for stochastic transitions, even if a state-action pair has been sampled multiple times, it is unlikely to have been sampled frequently enough to accurately reflect the underlying distribution.
These insufficiencies cause uncertainty in the sense that is unclear which parameter values are correct.
Unlike aleatoric uncertainty, parameter uncertainty can be reduced (and in the limit, eliminated \citep{de1937prevision}) by gathering more data.

Bayesian inference is a common approach to estimating parameter uncertainty.
However, analytically computing the posterior over parameters is intractable for large neural networks.
A significant body of research on Bayesian neural networks is concerned with approximating this posterior \citep{mackay1992bayesian, hinton1993keeping, barber1998ensemble, graves2011practical, gal2016dropout, gal2017concrete, li2017dropout}.

The statistical bootstrap is an alternative line of research for estimating parameter uncertainty \citep{efron1982jackknife}.
These methods train an \textit{ensemble} of neural networks, possibly on independent \textit{bootstrap samples} of the original training samples, and use the empirical parameter distribution of the ensemble to estimate parameter uncertainty \citep{lakshminarayanan2017simple, osband2016deep, pearce2018uncertainty, osband2018randomized}.
Ensemble-based methods can be interpreted as Bayesian approximations only under restricted settings \citep{fushiki2005nonparametric, fushiki2005bootstrap, osband2018randomized}, but share the goal of quantifying uncertainty due to insufficient data.

\noindent\textbf{Model Inadequacy:}
Model inadequacy refers to the model's hypothesis class being unable to express the underlying function generating the data.
In reinforcement learning, it is typical that the true dynamics function is not an element of the model's hypothesis class, both because this functional form is unknown and because the dynamics functions can be very complex.
Thus, even in the limit of infinite data, the parameter values that most accurately fit the dataset may not accurately predict transitions.
Error due to model inadequacy can only be resolved by increasing the capacity of the model.

\begin{figure}
   \vspace{0.7cm}
  \includegraphics[width=0.5\textwidth]{img/mve_pictorial.pdf}

  \caption{A depiction of MVE. A 3-step trajectory is simulated using an approximate model $\hat{p}$ from a state-action pair $(s_0, a_0)$; the simulated trajectory is used to construct multi-step TD targets; the TD-targets are combined using a weighted average; the action-value function $\hat{q}$ is updated toward the weighted average $U_{avg}$.}
   \label{fig:pictorial_mve}
\end{figure}

\subsection{Selective Planning with Ensembles}
Selective planning requires that the agent possess a mechanism for deciding when to use the model.
While ensembling is not the only existing approach (e.g. \citeauthor{xiao2019learning}) to designing this mechanism, it is the most prominent \cite{abbeel, kalweit}.
A particularly relevant selective planning method that uses ensembling is stochastic ensemble value expansion (STEVE) \citep{buckman2018sample}.
STEVE estimates parameter uncertainty by augmenting model-based value expansion (MVE) \cite{feinberg2018model}, an extension of DQN \cite{mnih2015human} in which model-simulated experience is used to evaluate the greedy policy, as is shown in Figure \ref{fig:pictorial_mve}.
STEVE uses the degree of agreement among an ensemble of neural networks to approximate the trustworthiness of the model for a particular rollout length.
Rollout lengths with low variance are given more weight in the update and rollout lengths with high variance are given less.

\begin{figure*}
\begin{center}
\includegraphics[width=\linewidth]{img/mve_capacity_lim_2.pdf}
      \vspace{-0.7cm}
  \caption{The performance of MVE in Acrobot, for varying model capacity, averaged over 30 runs with shaded region corresponding to standard error.
  When the model is given sufficient capacity to express the dynamics, MVE can increase performance, as is shown in the left two plots. However, when the model is lacking capacity, MVE can catastrophically damage learning, as is shown in the right two plots.}
  \label{fig:mve-fail}
  \end{center}
\end{figure*}

\section{Limited Capacity Can Harm Planning}\label{sec:mve_harm}

A main hypothesis of this work is that neglecting model inadequacy during planning can cause catastrophic failure.
To establish this idea, we begin by presenting an experiment examining the relationship between model capacity and performance in Acrobot \cite{sutton1996generalization}, a classic environment loosely based on a gymnast swinging on a highbar.
We ran MVE with four different network architectures for the value function:
one hidden layer with 64 hidden units, one hidden layer with 128 hidden units, two hidden layers with 64 hidden units each, and two hidden layers with 128 hidden units each.
For each network architecture, we determined the best setting for the step size, the batch size, and the replay memory size by sweeping over possible parameter configurations, as detailed in Appendix \ref{app:sweep}.

The results for the value function with one hidden layer with 128 hidden units, shown in Figure \ref{fig:mve-fail}, suggest that the relationship between capacity and performance is as anticipated.
When given sufficient capacity to learn a good model, MVE has the potential to improve the sample efficiency of DQN.
However, as capacity is decreased and the model becomes unable to accurately reflect the underlying dynamics, MVE harms learning progress.
The results for the other value function architectures, which can be found in Appendix \ref{app:baseline}, tell similar stories.

\section{Estimating Predictive Uncertainty Due to Limited Capacity Using Heteroscedasticity} \label{sec:error_limited_capacity}

While neural networks of reasonable sizes are perfectly capable of expressing the Acrobot dynamics function, this may not be the case in highly complex domains.
To defend against this possibility, it is desirable to have a mechanism for detecting predictive uncertainty due to model inadequacy.

We hypothesize that methods effective at detecting input-dependent noise should also be able to estimate predictive uncertainty due to model inadequacy.
The intuition behind this hypothesis is that a complex function can equally validly be considered a simple function with complex disturbances.
For example, $f \colon x \mapsto x + \sin(x)$ can be viewed as a linear function with input-dependent, deterministic disturbances.
Thus, methods designed for heteroscedastic regression may also quantify predictive uncertainty due to model inadequacy.
In contrast, parameter uncertainty methods may overlook predictive uncertainty due to model inadequacy and instead simply agree on the best parameter values within the hypothesis class in the limit of data.

\subsection{Heteroscedastic Regression} \label{sec:learned_variance_gaussian}
Neural networks are typically trained to output a point estimate as a function of the input.
When trained with mean-squared error, the probabilistic interpretation is that the point estimate corresponds to the mean of a Gaussian distribution with fixed input-independent variance $\sigma^2$: $p(y | \mathbf{x}) = \mathcal{N}(f_\mu(\mathbf{x}), \sigma^{2})$;
maximizing the likelihood in this case leads to least-squares regression.

An alternative is to assume that the variance is also input-dependent: $p(y | \mathbf{x}) = \mathcal{N}(f_\mu(\mathbf{x}), f_{\sigma^2}(\mathbf{x}))$, where $f_\mu(\mathbf{x})$ is the predicted mean and $f_{\sigma^2}(\mathbf{x})$ is the predicted variance.  Under this assumption, maximizing the likelihood leads to the following loss function \citep{nix1994estimating}:
\begin{equation}
\label{eq:heteroscadastic}
L_i(\theta) = \frac{(y_i - f_\mu(\mathbf{x}_i))^2}{2f_{\sigma^2}(\mathbf{x}_i)} + \frac{1}{2}\text{log}f_{\sigma^2}(\mathbf{x}_i).
\end{equation}
The learned variance $f_{\sigma^2}(\mathbf{x})$ can be predictive of stochasticity.
The network can incur less penalty in high-noise regions of the input space by predicting high variance.
We hypothesize that this learned variance should also be predictive of the errors in the context of limited capacity---the network can maintain a small loss by allowing the variance to be larger in regions where it lacks the capacity to make accurate predictions.

\subsection{Experimental Setup} \label{sec:regressionExample}
To examine this hypothesis, we contrast a subset of parameter uncertainty methods with heteroscedastic regression on a simple regression problem.

We constructed a dataset of 5,000 training examples using the function $y = x + \sin(\alpha x) + \sin(\beta x)$, where $\alpha=4$, $\beta=13$, and $x$ was drawn uniformly from the interval $(-1.0, 2.0)$ (see Figure \ref{fig:regression_example_fig}).
The inputs $x$ are drawn from a uniform distribution over the interval $(-1.0, 2.0)$.

\begin{figure}
\hspace{-2.0cm}
  \begin{center}\includegraphics[width=0.5\textwidth]{img/regression_example_fig.pdf}\end{center}
      \vspace{-0.7cm}
  \caption{The target function $y = x + \sin(4 x) + \sin(13 x)$ shown for the training interval (-1.0, 2.0). The blue points are 300 training samples drawn uniform randomly from the training interval.
  }
  \label{fig:regression_example_fig}
\end{figure}

We applied neural networks to this regression problem and varied the effective capacity of the model by reducing the number of layers and the number of hidden units.
In particular, we used neural networks with three degrees of complexity:  3 hidden layers with 64 hidden units each (referred to as large network), a single hidden layer with 2048 hidden units (medium-size network), and a single hidden layer with 64 hidden units (small network).
Experimental details are described in Appendix \ref{app:regression}.

We compared heteroscedastic regression with the following approaches for estimating parameter uncertainty.

\begin{figure*}
\begin{center}
\includegraphics[width=\linewidth]{img/regression_determ.pdf}
\caption{An evaluation of uncertainty methods on a simple regression problem when the model is subject to capacity limitations.
The ground truth function is in blue in all plots.
Each row presents the mean predictions and uncertainty estimates for a particularly-sized neural network after training for 300 epochs; each column presents the results for a particular uncertainty method.
Uncertainty estimates are represented by shaded intervals; the estimated predictive uncertainty arising from parameter uncertainty is in purple; the learned variance is in red; darker purple/red intervals show mean $\pm$ 1 standard-deviation and lighter intervals show mean $\pm$ 2 standard-deviation. Learning rate is $0.001$ for all methods; the results for other configurations of the learning rate (consistent with the results presented here) can be found in Appendix \ref{app:regression}.
}\label{fig:regression_results}
\end{center}
\end{figure*}

\subsubsection{Monte Carlo Dropout}
\citeauthor{gal2016dropout} (\citeyear{gal2016dropout}) proposed to use \textit{dropout} \cite{srivastava2014dropout} for obtaining uncertainty estimates from neural networks. Dropout is a regularization method which prevents overfitting by randomly dropping units during training.  Monte Carlo dropout estimates uncertainty by computing the variance of the predictions obtained by $M$ stochastic forward passes through the network. This technique can be interpreted through the lens of Bayesian inference; that is, the dropout distribution approximates the Bayesian posterior \cite{gal2016uncertainty}.

\subsubsection{Ensemble of Neural Networks} \label{sec:ensemble_nn}
Ensembling independently trains $K$ randomly initialized neural networks.
The variance in the predictions of individual networks is used to estimate predictive uncertainty arising from parameter uncertainty \cite{lakshminarayanan2017simple, osband2016deep, pearce2018uncertainty}. Intuitively, the individual networks in an ensemble should make similar predictions in the regions of the input space where sufficient samples have been observed, while making dissimilar predictions elsewhere.

\subsubsection{Randomized Prior Functions} \label{sec:rpf}
Randomized prior functions (RPF) \cite{osband2018randomized} are an extension of ensembling.
Each network in the ensemble is coupled with a random but fixed \textit{prior} function---a randomly initialized neural network whose weights remain unchanged during training.
The prediction of an individual ensemble member is the sum of its trainable network and its prior function.
For Gaussian linear models, this approach is equivalent to exact Bayesian inference \cite{osband2018randomized}.

\subsubsection{Randomized Prior Functions With Bootstrapping} \label{sec:rpf_b}
Bootstrapping can be combined with both randomized prior functions \cite{osband2018randomized} and ensembling \cite{osband2016deep}.
We focus on the former as it has been noted to provide better uncertainty estimates \cite{osband2018randomized}.

\subsection{Experimental Results}

The results of applying each of the above methods to the regression problem are shown in Figure \ref{fig:regression_results} for a single configuration of the learning rate. We found the results to be consistent across learning rate configurations (see Appendix \ref{app:regression} for additional results).

With a sufficiently powerful network (large network), the ensemble learns to accurately predict the target function, and the predictive variance of the ensemble (purple) appropriately assesses the predictive uncertainty---the ensemble variance is large outside the training distribution.
We observe the same effect for other parameter uncertainty methods.

As the capacity is reduced (small and medium-sized networks), all methods fail to fit the target function accurately over the entire input space.
But whereas learned variance reliably reflects the errors within the training distribution, the parameter uncertainty methods fail to do so.
These results support the idea that parameter uncertainty is insufficient for selective planning in the face of limited capacity.
Instead, they suggest that a combination of a parameter uncertainty and model inadequacy would yield a more robust error detection mechanism than either individually, as is indicated by the rightmost column, which combines learned variance with ensembling.

\section{Selective Planning} \label{sec:sec_selective_mve}

In this section, we investigate the utility of learned variance in the context of model-based reinforcement learning;
in particular, we ask whether learned variance can be used to plan selectively with a low-capacity model that otherwise leads to the planning failures observed in Section \ref{sec:mve_harm}.

Toward this end, we describe a technique using learned variance to do selective model-based value expansion.
Given a maximum rollout length $H$, consider the weighted-average of h-step targets (see Figure \ref{fig:pictorial_mve}):
\begin{equation*}
\label{eq:h-step-target-generic}
U_{avg}(s_0, a_0) = \sum_{h=1}^{H} w_h(s_0, a_0) U_h(s_0, a_0).
\end{equation*}
We would like the weight on an h-step target to be inversely related to the cumulative uncertainty $\sigma_{1:h}^2(s_0, a_0) = \sum_{i=1}^h \sigma_i^2(s_0, a_0)$ over the h-step trajectory.
Given the cumulative uncertainty of the targets, we determine the weight of an individual target by computing the softmax
\begin{equation}
\label{eq:h-step-target}
w_h(s_0, a_0)  = \frac{\text{exp} (-\sigma_{1:h}^2(s_0, a_0)/\tau)}{\sum_{i=1}^{H} \text{exp} (-\sigma_{1:i}^2(s_0, a_0)/\tau)}
\end{equation}
where $\tau$ is a hyper-parameter which regulates the weighting's sensitivity to the predicted uncertainty.

To handle the multidimensional output space, we assume independence across different dimensions of the state (i.e., an isotropic Gaussian assumption) and learn a separate variance for each dimension.
To acquire a scalar value, we sum the variance values across the dimensions of the state space (i.e., we use the trace of the diagonal covariance matrix).
Further implementation details, including the range of values for the parameter sweep, and the configuration of the rest of the hyper-parameters are included in Appendix \ref{app:selective}.

\begin{figure*}
\begin{center}
\includegraphics[width=1.02\linewidth]{img/acrobot_selectivemve_2.pdf}
  \vspace{-0.7cm}
  \caption{Results of selective MVE ($\tau=0.1$). The learning curves are averaged over 30 runs; the shaded regions show the standard error. Selective MVE with models of 4 hidden units (d) and 16 hidden units (c) not only matches the asymptotic performance of DQN, but also achieves better sample-efficiency than the DQN baseline. Selective MVE improves the sample-efficiency even in the case of larger models consisting of 64 hidden units (b) and 128 hidden units (a). }
  \end{center}
  \label{fig:acrobot_selectivemve_results}
\end{figure*}
\subsection{Selective MVE Avoids Catastrophic Failure}

In this section, we apply the planning algorithm described above, which we call selective MVE, to Acrobot.
We compare selective MVE using learned variance to standard MVE, DQN with no model-based updates, and selective MVE using the true squared error (given by an oracle) to weight its rollouts.
The results, presented in Figure \ref{fig:acrobot_selectivemve_results}, show that selective MVE under capacity constraints (models with 4 and 16 hidden units) not only matches the asymptotic performance of DQN, effectively avoiding planning failures, but is also more sample-efficient than the DQN baseline.

Interestingly, selective MVE improves sample-efficiency even in the case of larger models consisting of 64 and 128 hidden units.
This may indicate that selective planning allows the agent to make effective use of the model early in training when many of its predictions are unreliable but some are accurate.

To get a better of sense of the robustness of selective MVE to model errors, we compute the expected rollout length of each of the four model sizes for both the learned variance and the true error, as is shown in Figure \ref{fig:acrobot_selectivemve_exp_rollout}.
(The expected rollout length of an update is the weighted average over the rollout lengths used in equation \ref{eq:h-step-target}.)
There is a clear ordering in the expected rollout length of the four models;
$h$--step targets consisting of longer trajectories are given relatively more weight when the model is larger (and as a result, more accurate).
Selective MVE with the smallest model of 4 hidden units does not use the model as much as its variants with bigger models, but the limited use is still sufficient to improve the sample efficiency of DQN, while preventing model inaccuracies from hurting control performance.

\begin{figure}[h]
    \hspace{-7mm}
  \includegraphics[width=0.55\textwidth]{img/acrobot_rollout_length_2.pdf}
    \vspace{-0.2cm}
  \caption{The plots contrast the expected rollout length of selective MVE ($\tau=0.1$) for the known error (left) and the learned variance (right). Each reported curve is the average of 30 runs; the shaded regions show the standard error.}
  \label{fig:acrobot_selectivemve_exp_rollout}
\end{figure}

\begin{figure}
\vspace{-0.7cm}
\begin{center}
  \includegraphics[width=0.5\textwidth]{img/mve_hetero_2.pdf}
  \vspace{-0.55cm}
  \caption{Performance of MVE when the model is learned using the heteroscedastic regression's loss function. The learning curves are averaged over 10 runs; the shaded regions show the standard error.}
  \label{fig:loss-plot}
  \end{center}
  \vspace{-0.5cm}
\end{figure}

\subsubsection{Improved Performance Cannot be Attributed to the Loss Function}
To verify that the gains in performance are not due to the change in loss function (selective MVE uses a heteroscedastic regression loss function, whereas MVE uses MSE---a homoscedastic loss function) we evaluated MVE with the same loss function as that of selective MVE.
The results, presented in Figure \ref{fig:loss-plot}, suggest that simply changing the loss function does not lead to an accurate model, and that the model still needs to be used selectively.

\subsubsection{Improved Performance Cannot be Replicated With Ensembling}
To verify that the same performance gains could not be achieved from ensembling, we applied a variant of selective MVE using ensemble variance, rather than learned variance.
Selective MVE with ensemble variance resembles STEVE, except that it uses variance over state predictions instead of over value predictions and softmax instead of inverse weightings.
While selective MVE with ensemble variance performs comparably to selective MVE with learned variance early in training, the performance of the planner using ensemble variance consistently collapses later in training, presumably as a result of the ensemble converging to similar, incorrect parameter values.
Results for the architecture with 4 hidden units are shown in Figure \ref{fig:ensemble-fail}.

\begin{figure}
\begin{center}
  \includegraphics[width=0.5\textwidth]{img/ensemble_performance_1M_2.pdf}
    \vspace{-0.1cm}
  \caption{Results of selective MVE ($\tau=0.1$) over 500 thousand steps. The learning curves are averaged over 30 runs; the shaded regions show the standard error. Selective MVE with ensemble variance initially offers performance competitive with that of learned variance, but ultimately collapses.}
  \label{fig:ensemble-fail}
  \end{center}
\end{figure}

\section{Complementary Nature of Parameter Uncertainty and Model Inadequacy}
\begin{figure}[t]
\begin{center}
  \includegraphics[width=0.5\textwidth]{img/correlation_plot.pdf}
  \vspace{0.22cm}
  \caption{Correlation of the true squared error with the learned variance, the ensemble variance, and the combined variance over the course of the agent's training. Each reported curve is the average of 30 runs; the shaded regions show the standard error.}
  \label{fig:correlation_plot}
  \end{center}
\end{figure}

In Section \ref{sec:error_limited_capacity}, we argued that predictive uncertainty arising from parameter uncertainty is by itself is insufficient for selective planning under capacity constraints, and needs to be used in combination with predictive uncertainty arising from model inadequacy.
In Section \ref{sec:sec_selective_mve}, we used learned variance to estimate predictive uncertainty arising from model inadequacy, and found that it can ensure robust planning under capacity limitations.
In this section, we emphasize the complementary nature of parameter uncertainty and model inadequacy.

We extend our Acrobot example and train an ensemble of neural networks with heteroscedastic loss functions.
We use an ensemble of 20 single hidden layer networks with 4 hidden units, and
use the mean value of the ensemble to make a prediction.
To compute the variance, we consider the ensemble as a uniform mixture over Gaussians, along each dimension.
We compute the variance of the mixture model along each dimension and sum the variances as we did with heteroscedastic regression to get a scalar value.

To evaluate the efficacy of the combined variance relative to learned variance and ensembles variance, we sample a batch of transitions from the replay buffer at every time-step and compute the correlation of each variance with the true mean squared error.
The results, shown in Figure \ref{fig:correlation_plot}, suggest that in the context of limited capacity: 1) Ensemble variance becomes a less useful indicator of error as training progresses, presumably because the ensemble tends to converge to similar predictions.
2) Learned variance becomes more predictive of error as it learns from more data.
3) Combined variance is more strongly correlated with true error than both learned variance and ensemble variance over the entire course of training.
While existing work \cite{sergey} has investigated combining heteroscedastic regression with ensembling in the context of non-deterministic domains, our results suggest that doing so has positive benefits under capacity limitations even in the absence of stochasticity.

\section{Conclusion}
In this work, we investigated the idea of selective planning: the agent should plan only in parts of the state space where the model is accurate.
We highlighted the importance of \textit{model inadequacy} for selective planning under limited model capacity.
Our experiments suggest that heteroscedastic regression, under an isotropic Gaussian assumption, can reveal the presence of error due to model inadequacy, whereas methods for quantifying parameter uncertainty do not do so reliably.
In the context of model-based reinforcement learning, we show that incorporating learned variance into planning can outperform the equivalent model-free method, even when using the model non-selectively would lead to catastrophic failure.
Lastly, we offer evidence that ensembling and heteroscedastic regression have complementary strengths, suggesting that their combination is a more robust selective planning mechanism than either in isolation.

\section{Acknowledgements}
This material is based upon work supported in part by the National Science Foundation under Grant No. IIS1939827, the Alberta Machine Intelligence Institute, and the Canadian Institute For Advanced Research.

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Home | Oz Sultan
Tags: ricin | trump | mail | mattis
Ricin and Rise of Decentralized Domestic Bioterror Threats
U.S. Defense Department personnel, wearing protective suits, screen mail as it arrives at a facility near the Pentagon in Washington, D.C., on October 2, 2018. Two or more packages delivered to the Pentagon this week were suspected to contain the deadly poison ricin, an official said. (Thomas Watkins/AFP/Getty Images)
By Oz Sultan
Wednesday, 03 October 2018 05:13 PM Current | Bio | Archive
In the past, we've discussed the rise of Antifa and regional terror risks from Jihadist actors and extremist groups. However in the past few weeks, we've seen a rise in decentralized attacks that mimic many of the hallmarks used by terrorist groups in the past. The concern with these attacks is that they are domestic threats that are increasing in both frequency and distribution — which are now returning to bioterror.
In the U.S., we've seen a return to the type of decentralized attacks that mimic mail attacks of the 1990s — only in this case the attacks are broad-based focused on the president and a number of senior officials, including General Mattis. Parallel path to this, we've seen the escalation of protests moving to directed attacks on Senator Cruz and a number of GOP leaders.
ABC confirmed yesterday that the Secret Service was cooperating with investigators on confirmed Ricin sent to President Trump's office by mail. Meanwhile NBC confirmed that Senator Ted Cruz and Secretary of Defense Mattis and Chief of Naval Operations Adm. John Richardson's offices also received packages containing Ricin.
The NCBI notes that U.S. preparedness for bioterror attacks has room for improvement and this week's attacks that have been potentially linked to an Ex-Navy, potentially lone-wolf actor underscore the need to do more.
Ricin and other biological agents are not as effectively tracked and traced as radiological and other high risk terror agents (for example — the FBI keeps watch for the purchase of ingredients to make Fertilizer bombs or TATP [a common explosive used by ISIS]).
These attacks are additionally concerning, as they come on the anniversary of the 2001 Anthrax attacks and underscore the risk that there are more attacks to come.
Dr. Amesh Adalja noted in The Hill that:
"Because of the ubiquity of castor beans and the ease of basic weaponization —which is significantly easier than developing other environmental agents such as anthrax, tularemia, plague, or botulism into a bioweapon — additional ricin attacks should be expected. In 2013, for instance, ricin was mailed to then-President Obama, Sen. Roger Wicker (R-Miss.) and Mississippi Judge Sadie Holland and resulted in a multi-decade jail-term for the perpetrator."
While America faces a new biological threat that we must contend with, Europe is facing a less severe but equally menacing challenge.
In Europe, London experienced another knife attack in a continuation of what is now a seven year high in the UK.
UK knife attacks have become a mixture of gang, lone wolf, and lone jihadist risk scenarios — that the UK government is ill prepared to tackle.
To add to this, Germany experienced an attack by a white nationalists, who performed a Nazi salute after stabbing a journalist.
Addressing the Risks and Remediation
These attacks, driven by individuals loosely connected to terrorist, gang, or extremist causes is a shift from the cell-based or organized domestic terror that we saw through the 00's.
The rise of extremist groups from jihadists, to white nationalists, to Antifa and now a return to bioterror underscore a change in the threats that we need to be concerned with.
Social Media organization in private groups and channels on known platforms such as Facebook and Twitter have given way to decentralized and individual actor opportunities on new social platforms and international forums. Facebook itself was hacked and as of yesterday, it's more than likely that your profile is for sale on the dark web.
Tracking and stopping these decentralized actors will require a new approach that involves cooperation between state and local agencies — as well as data sharing with international organizations, such as Interpol.
We caught a break with the current Ricin attack, as the suspected sender was reportedly simplistic enough to put his return information on the envelopes — however future forward, we'll need to start looking at these domestic risk factors with equal seriousness to the challenges international terror organizations bring.
Oz Sultan is a leading Big Data and counterterrorism expert who focuses on anti-recruiting and ISIS counterterror (CT) research within crypto and social media. He also advises a number of Blockchain companies and is a Board Member of the Homeland Security Foundation of America. To read more of his reports — Click Here Now.
Posts by Oz Sultan
Assessing Facial Recognition Technology After the Strasbourg Attack
Pipe Bomb Attacks Also Reflect White Powder Trend
View More Posts by Oz Sultan
OzSultan
In the past, we've discussed the rise of Antifa and regional terror risks from Jihadist actors and extremist groups.
ricin, trump, mail, mattis
Wednesday, 03 October 2018 05:13 PM

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← Open primaries.
Oliver Stone's revenge. →
An internet diet.
Ms. Lippman claims to be on an internet diet. As she is a very disciplined person, I believe it. (I add that I'm honored she includes this site in her restricted surfing, particularly considering it has no supplemental vitamin or minerals.) What's more, I think she's on to something. I didn't miss the internet (too much) during our vacation, and I'm thinking I want to be more analog for a while.
So I'm going on a diet. I will not be neglecting this site. Too much. Same daily updates, perhaps less bloggage. Maybe you won't notice it at all, but I'm going to restrict my time spent a) blogging and b) looking for things to blog about to 45 minutes a day, 60 at most. I have a few writing opportunities I want to explore, and if the mile of tombstones* this year has reminded me of anything, it's that we don't have all the time in the world, just some of it. Wouldn't it be stupid to lie on your deathbed and think, "I spent it all blogging"? I think so.
Also, I need to do more video. Even though I am unemployable by traditional media, I like to keep the skills sharp.
What I mainly think I'm going to do is stop reading the sites that bug me. While there's a certain scab-picking satisfaction in seeing What That Idiot Has to Say Today, it's just, alas, a waste of time. So long, Jim Lileks. Au revoir, Rod Dreher. Farewell, about a dozen other blogs. It was fun while it lasted, and besides, I'm still reading Roy, who will keep us updated on the highlights.
* turn of phrase borrowed from Thomas McGuane, who used it as the title of an essay about a rash of deaths in his family
OK, then. What a nice weekend. Spent it at Eastern Market (July! Time for corn, peaches, snow peas, bok choy, sugar snaps, tomatoes, beets, weensy little carrots and yes I made two trips to the car), sailing, moviegoing ("Journey to the Center of the Earth," which will go on my parental-duty roster in the plus column, but otherwise be entirely forgotten in a matter of days) and, Sunday, a Tigers game. As a recent transplant, I really don't give a crap about the Tiger Stadium demolition, despite the Free Press' dedication to covering every swing of the wrecking ball, and besides, Comerica is hardly a dump. It was hot and sweaty in the sun, but the seats were great (thanks, Michael and Diane) and the Tigers won. Pudge Rodriguez went four-for-four — a Hot Pudge Sunday — and there were a couple of nice homers. And the heat wasn't even that bad; fortunately, there was beer.
Friday night at the movies was something else, however — we went to the 5 p.m. show and came out in the midst of Macomb County Friday Night, a vast gathering at a new "lifestyle center" mall up in the northern 'burbs. "Lifestyle center" = open-air. Their gimmick is, they allow dogs, and every time I go there I wonder if this will be the day disaster strikes. Because there are an awful lot of stupid people in the world, people who think dogs "enjoy" a Friday night spent strolling at the mall, in the company of hundreds of people and dozens of strange dogs, some of which are barely under control in the first place. Since we were last there the mall added an outdoor splash fountain and climbable play area, so add a bunch of toddlers to the mix, too. Every time I go there I witness at least one dog argument barely avoided, sometimes between, oh, an 80-pound boxer and a 100-pound lab, both straining at the ends of their leashes, which are held by 110-pound women who simply don't have a clue. About anything.
Also, these trips enable me to see how many people think it is normal and admirable to put clothes on dogs. I'm not talking a bandanna around the neck, either. I ask you.
So, a little bit of bloggage:
Mitch Albom, I beg you, take the buyout. A grateful readership would thank you. I would, anyway.
And one final housekeeping note: This week is when I'm collecting the last of my doctor's 50th-birthday presents, the one that requires a special diet, Miralax and general anesthesia. So if I disappear for a couple of days, please try not to picture what I'll be doing. 'kay?
Posted at 10:11 am in Media, Same ol' same ol' |
37 responses to "An internet diet."
Dorothy said on July 28, 2008 at 10:21 am
Speaking of dogs, how's Spriggy since last week?
nancy said on July 28, 2008 at 10:26 am
Better, thanks. Plumping back up, still eating like a little hog. I don't think we'll leave him for that long again. He's just not up for it anymore.
LAMary said on July 28, 2008 at 10:47 am
I love seeing dogs dressed in Juicy Couture outfits, carried through Nordstroms either in little Louis Vuitton doggie totes or under their mistress's arm. I'm thinking of doing this with Max if I can find Juicy Couture doggie clothes for 140 pound boxer/dane mixes.
People, dogs do not want to be carried around or walked through shopping malls unless they can scavenge the food court.
Two years ago I went to the LA Auto show with the kids and inhouse Brit. In the ladies' room there was a woman with a chihuahua. The dog had crapped all over the interior of the pricey doggie carrier and the woman was washing it out into the ladies' room sink. Dog diarrhea in the sink where I needed to wash my hands. This is just wrong. I love dogs a lot but I wouldn't take them to the auto show and I wouldn't expect people to tolerate my dumping their crap into the sink.
Sue said on July 28, 2008 at 11:30 am
Daisy the German shorthair mix is currently visiting us while her owners are up north for a week. Compatible households, of course: Daisy will be sleeping with us and hogging the couch just like she does at her real house. If you don't mind the long drive to drop him off, Spriggy is welcome here anytime. He won't lose weight, I promise.
coozledad said on July 28, 2008 at 11:48 am
Roy's a good one to stick with. I'm still laughing at the image of Jonah Goldberg in a press box with a bat up his ass.
ellen said on July 28, 2008 at 12:27 pm
I had that "birthday gift" a couple of weeks ago. Best advice someone gave to me: Make the "special drink" the night before and chill it in the fridge. Keep it as cold as possible and drink it over a lot of ice. It goes down a lot easier that way. Also, if you are lucky, you will be done in 3 liters, instead of the full 4. The actual procedure was nothing. A pleasant nap, just like a previous commenter here (LAMary? Dorothy?) said it would be. And the rest of the afternoon was a nice haze, too. I have no memory of that day at all.
Julie Robinson said on July 28, 2008 at 12:31 pm
General anesthesia? It was just a local when a certain significant other in my life had it done. That's what I was counting on when it's my turn in September. General anesthesia scares the crap out of me. No pun intended.
Dorothy said on July 28, 2008 at 12:40 pm
The prep is worse than the procedure. I have no memory of the procedure. I must have been nice and drowsy to not even realize what happened. And afterwards I felt just fine. In 5.5 years I will probably have another, and not be nearly as full of trepidation as the first one was.
And Julie, even if it was unintended, it was a fun pun!
Sue said on July 28, 2008 at 12:42 pm
Julie, I think it's a twilight kind of thing usually, neither local nor general. An iv is started and you get an injection, which makes you very sleepy and helps you not to remember anything. No intubation, no anesthesiologist. Talk to your doc.
nancy said on July 28, 2008 at 12:54 pm
Perhaps "general anesthesia" is a medical term, but I understand it to be any procedure where you're all the way knocked out, as opposed to awake. Sue is right — they told me it would be some sort of knock-out drug but I'd be breathing on my own.
Connie said on July 28, 2008 at 12:55 pm
I am just back from eight days with not only no internet, but no cell phone signal without a drive as well. Missed the internet very little which surprised me.
Our old man Shih Tzu stayed home with the kid – who turned 21 yesterday. The kid, not the dog. The dog is suffering from severe arthritis and congestive heart failure and we know we are going to have make the big sleep decision sometime soon.
Glen Lake Sleeping Bear was wonderful as always, despite the overcast nights – no star gazing all week. We spent way less than usual, mostly because there were no seadoos to gas up.
Back to work this a.m., to find ONLY two managers waiting for me to discuss whatever it is went on in their dept while I was gone.
Just finished my first canning batch for the season – currant jelly. This has got to be the latest first ever. We have one tomato that is almost ripe, and our first green beans (the three plants that survived the second flood) are about 1/2 inch long. Any day now! We bought our first local corn yesterday – it's up to almost $6 a dozen this year, almost double last year's price. And it wasn't very good, either; kind of like what you used to get before they started planting the supersweets.
Mindy said on July 28, 2008 at 1:00 pm
Looks like I've been on an internet diet for six months at least. Never thought of it as such, though. So far I've gained a much cleaner house, a few completed novels, and time to knit. The current project will be off the needles in plenty of time for fall. It was supposed to be ready for fall of 2006. But I was surfing.
http://www.berroco.com/exclusives/picchu/picchu.html
I've also managed to spend plenty of my found time drinking wine on the deck in the beautiful weather we've had lately. But it's a lovelier and more relaxing way to waste time. I hope you gain at least as much on your diet as I have on mine.
moe99 said on July 28, 2008 at 1:23 pm
Well my oldest son spent the night at The Hampton Inn next to the Detroit airport due to what was billed as weather problems back in NYC. Really. Thought of doing a call out here in case he couldn't find a place to stay, but it all turned out ok. So far. He thought I was a bit over the top when I told him, all 22 years of him, that he could NOT go wander around Detroit proper at night. He's really only experienced Seattle, the Twin Cities, and a bit of the cleaned up NYC so he has no idea what's out there.
ellen said on July 28, 2008 at 2:00 pm
You aren't knocked out; you are sedated via IV. You are not unconscious, because, as my gastroenterologist explained, they might need to ask you to shift your position slightly during the procedure. You are awake and the thing is happening, but you are so doped up you don't know/care, you don't feel anything, and you have no memory of it afterward. I think they do vasectomies the same way.
coozledad said on July 28, 2008 at 2:14 pm
That's always been one of my fears. They give me the IV Valium and Demerol drip, and the well worn doormat of my opiate receptors says "That's pretty good. I'll have another when you get a chance". And I wind up feeling the entire procedure.
Gasman said on July 28, 2008 at 2:14 pm
I've seen you frequently rant about Mitch Albom, however, as I don't read his stuff very often, I've not not been too offended by him. His current column is something to behold. If this is typical Albom, I understand your rants. Takes up much space without actually writing very much.
Must.
nancy said on July 28, 2008 at 2:19 pm
That's pretty typical for the non-sports stuff, which is all I read of his. Take a topic, stake out a pretty safe position on it, find a catch phrase you can repeat every three grafs, add white space.
He's probably knocking down about 300K from the paper alone for that.
brian stouder said on July 28, 2008 at 2:31 pm
So if I disappear for a couple of days, please try not to picture what I'll be doing.
Just wanted to comment on the elegant construction of this post, with the internet purge cleverly connected to the joke at the end, making for an interesting post all around!
My own tolerance of silly bull***t is declining just as rapidly as my age is advancing, and the internet is basically a big cow pasture, when it comes to loads of dung just waiting for your bare feet. Consequently, the endless political cowpie sites turn me off (after spending years romping around in a few of them) pretty completely. This website, plus msnbc news is most often where I go. (and true enough, I'll follow links from here to elsewhere sometimes). The BBC F1 site is another occasional stop.
I can see how lots of the stuff on the internet glitters and charms young folks; it's all so new and different and odd and "in" and exciting. But in the end, it has no more charms than have always existed, whether in books or happenings or the stories our friends tell us (and indeed, the internet is no more truthful than any of those other sources!)
Saturday Grant and I were at the Indianapolis Motor Speedway for qualifying and practice (while the girls ran off to the Children's Museum; Pam posted some pictures); a long, hot, entertaining day. Late in the afternoon, hoots and hollers arose off to my right, and I saw a very attractive, well built and fairly drunk blonde, and her more completely drunk, buzz-headed, 6-pack ab husband or boyfriend came into view, and the attractive blonde pulled her strapless top down, and actively displayed her undulating breasts. Great stuff! She was very well built, and had a fair amount of stagecraft, as she then popped her top right back up again. But they were working their way toward us, and the novelty wore off after the 6th or 7th time she revealed herself – so that I was beginning to feel more like a concerned dad than a guy who just won five dollars on a lottery ticket. Ultimately they sat within 40 feet of us, on the same row in the bleachers, where the act continued; she teased and moved and flashed – on cue from her man, who was wearing a bizarre $40 Ed Hardy tee shirt (I Googled Ed Hardy later, and learned about it) as he circulated amongst other fans and spoke to them about….who knows what? Seriously, she looked too good, and he was acting too strangely. Were they drumming up business for a gentleman's club? Whatever the game was, it got old in a hurry, to me, but not to Grant! (Yes – the bottomline is – I'm getting old!)
Jen said on July 28, 2008 at 2:53 pm
Nancy: Dave Barry wrote a column back in February about the same procedure you're going to have. I thought it was amusing but, being only 23, I haven't experienced that "joy" yet. My editor, who is over 50, said he hadn't laughed so hard in years as he did reading the column. It's here: http://www.miamiherald.com/548/story/427603.html
Mitch Albom strikes me as a HUGE snot. I don't think big-name actors are doing comic book movies because it's the only thing Hollywood will do, or because it's the only way they get paid. They're doing it because the movies are MUCH better than they were. Come on – "The Dark Knight" is written better than the '60s TV show and its movie (which I also loved). And it's not like it's a really new thing, either. Jack Nicholson was already known as a good actor in 1989 when he played the Joker in Tim Burton's "Batman." It's not like it's a new phenomenon. Just because a movie is based on a comic book does not mean it can't be a good story. What a turd!
Incidentally, your reading of Mitch Albom columns, since you know you're just going to get ticked, reminds me of my father, who reads a certain columnist in a certain newspaper every week just because he hates the columnist's writing SO much and loves to rant about it.
James said on July 28, 2008 at 3:17 pm
"I think they do vasectomies the same way."
No. You get a local, and are fully aware. Trust me. The doctor was teaching some young lady how to do the procedure during mine, 16 years ago or so…
Sue said on July 28, 2008 at 4:04 pm
Loved Dave Barry's column. He's absolutely right about waking up feeling mellow. That's niiiice.
The office I work in reviews submittals for subdivisions in our city. One rendering that came in several years ago reminded me stongly of the large intestine. Really. There was an ascending, transverse and descending part, plus a large area of green space right in the middle where the small intestine would normally be shoved. The only thing missing was a tiny cul-de-sac where the appendix should be. Plus it was backwards. Since "Relationship to Human Anatomy" is not one of the review criteria, I didn't bring it up to my boss.
Jeff (the mild-mannered one) said on July 28, 2008 at 5:07 pm
Nancy, not true on yer blog having "no supplemental vitamin or minerals;" isn't there a full recommended daily allowance of Irony in each serving?
Dexter said on July 28, 2008 at 5:36 pm
I remember when Mitch came to the Freep, replacing Mike Downey, who moved to the LA Times and is now writing "In the Wake of the News" for da Trib in Chicago, which is a hallowed position given to a sports sage with great respect by peer and reader alike.
I really like Downey but quickly warmed up to Mitch and read every column for years . He did great work , giving balance to stories and assumptions ,"bad raps" about controversial athletes like Chris Webber.
I was a fan of his radio show on WJR but now I never listen to that crummy right wing station.
I was moved by "…Maury" and Eddie, the carnival worker in "…Heaven".
In the past few years I only occasionally read his stuff, though, and this column Nancy linked and others lately are the reasons why.
I am still a fan of his; I have never seen his B'way plays, but I like his NYC based columns. His column on the great Bobby Short and the Carlyle Hotel lounge was my favorite. I could just hear Short telling of Hildegard and other stories.
For those of you who have had enough, take pleasure in knowing that one man who was not a fan had a solution for his frustrations with Mitch. In 1984 Guillermo Hernandez, the best reliever in baseball , dumped a huge bucket of ice water on Mitch's head in the Tigers' locker room. Mitch still hates that fucker for that. He wrote about it for years.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The drug is Verset, sometimes combined w/Demerol. I was nervous as a whore-in-church before, for no reason. My doc was late and I was on the table for 45 minutes making small talk with two nurses and the doc arrived, the nurse says, "here comes the good stuff", drip drip, and off to "Twilight Land". I do recall a sound like a far-off sewer-digging machine, then it was over and I was making claims the doctor wanted to see me in his office right away…I had dreamed it. And I do recall I was asked to shift positions once sometime during the procedure. I go to the dentist regularly, and the c-scope was easier than ANY dentist visit.
a total non-sequitur: I just got the notice in the mail for the 23rd annual Lincoln Colloquium, which will commemorate the 150th anniversary of the Lincoln-Douglas debates, and which features lectures by Douglas Wilson, Rodney Davis, Allen Guelzo, James McPherson, Garry Wills, and David Zarefsky.
It will be held at Knox College at Galesburg, Illinois on October 11 (a Saturday)….and wild horses couldn't keep me away!!
It sounds like it will be absolutely and wonderfully enthralling, coming as it does amidst the decsive stages of our presidential election…and the cost is $25!!! (For a comparison, I paid $40 – after popcorn and pop got added, to take the young folks to Dark Knight)….but I suppose I'll come home with several more great books to read, so that has to be factored in
Connie said on July 28, 2008 at 7:08 pm
So why are you unemployable by traditional media? Surely not the Albom bashing.
Linda said on July 28, 2008 at 9:33 pm
I can understand the internet diet. I stayed away from a website full of idiots for Lent. It was great. Gave me a new perspective. And I believe Roy exists so that we don't have to read wingnut idiots.
del said on July 28, 2008 at 10:07 pm
I often think of the Willie Hernandez episode. Albom had some rather unkind things to say about Willie — who had reverted to his birth name, Guillermo, a decision that Albom found silly as I recall. A writer can wound with his words. And after the ice-bath Albom was on a perpetual rant against Hernandez.
Dexter said on July 28, 2008 at 11:58 pm
We all know the bees are going away. In China , they have to pollinate the fruit trees by hand.
Can you imagine that? Talk about labor-intensive!
And I still miss Joe Strummer. Here he laments about the bees.
http://www.youtube.com/watch?v=9pYwPc6UNmo
MarkH said on July 29, 2008 at 2:59 am
Jen, thanks for sharing the Barry column; a hoot, and very accurate, as was Ellen's description.
I had mine two years ago, at 54, and the prep was definitely worse than the procedure. In addition to the previous night's evacuation procedure, there was a prep IV prior to the real anethesia, and, as the nurse warned, it burned going in my wrist. Then I was out, and woke up a second later, it seemed. Smiles all around; a healthy colon, aside from a slight "ulcerization", whatever that was. Good results added to the mellowness. They are examining you as the tube exits, with the colon carefully inflated so the bright light shows the camera EVERYTHING. Your colon will never look as clean again, Nancy, so be sure to ask for the photos (well, maybe not).
You won't want to do anything the rest of the day. My boss tried otherwise and bragged how he came back to work for half a day. My buddy here confirmed he did come back, but he was worthless and looked like hell.
On the flip side, as of last week, I am helping my close friend deal with a likely diagnosis of the worst news. He had symptoms recently, and at only 45, had not considered he needed the colonoscopy. After initial tests, he was told by his doctor to prepare himself as he goes to Billings next week for the colonoscopy, and get the final determination. I'm praying for him, as his father passed away last Christmas from a malignant brain tumor.
John said on July 29, 2008 at 7:54 am
We all looked at the pictures from the playground on fire, but when you finally get an eye-popping photo op, you don't provide diddley!
Jeff (the mild-mannered one) said on July 29, 2008 at 8:25 am
If you dislike foul language, don't click this link; if you dislike being edited, click this link — http://www.guardian.co.uk/media/2008/jul/23/mediamonkey
brian stouder said on July 29, 2008 at 8:44 am
John – I think that was the first time I went to the speedway without a camera!
Kirk said on July 29, 2008 at 10:35 am
She leaves a movie trailer, with a "McCain for president" ad right beneath it, and closes off the comments. What twisted form of revenge is this?
I already have nightmares about the creep in chief, but I'll go see it anyway. Stone is actually far too kind:They got an actress to play Babs.
The preview makes the movie look like a very healthy (for our republican democracy) trip to the dunk tank for the crew currently in power.
But, further to our discussion about age and cowpies, we should remember the great concluding voice-over by George C Scott in the movie Patton:
For over a thousand years Roman conquerors returning from the wars enjoyed the honor of triumph, a tumultuous parade. The conqueror rode in a triumphal chariot, the dazed prisoners [in this case, White House staffers!] walking in chains before him. And a slave stood behind the conqueror holding a golden crown and whispering in his ear a warning: that all glory is fleeting."
by way of saying, we've seen this sort of thing before, and we'll certainly see it again, if we live long enough
roy edroso said on July 29, 2008 at 7:11 pm
So, I see, it's all up to me. Thanks a fuck of a lot. Nothing makes this job easier than knowing one of the smarter people doesn't have my back.
One of these days I'll accumulate your level of wisdom and go back to hypnotizing chickens. Till then enjoy your new freedom and spare me a lonesome tear sometimes. It's nasty out here.
Dorothy • nancy • LAMary • Sue • coozledad • ellen • Julie Robinson • Dorothy • Sue • nancy • Connie • Sue • Mindy • moe99 • ellen • coozledad • Gasman • nancy • brian stouder • Jen • James • Sue • Jeff (the mild-mannered one) • Dexter • brian stouder • Connie • Linda • del • Dexter • MarkH • John • Jeff (the mild-mannered one) • brian stouder • Kirk • coozledad • brian stouder • roy edroso • and YOU.


text:
Lift off for development plans at Harwell
Construction + development
We're excited to announce that planning has been approved today for two new buildings on our scheme at Harwell Campus near Oxford.
Construction is due to begin this month on Genesis, a new digital, design and manufacturing building that will house 33,300 sq ft of engineering space, laboratories and offices as well as providing additional workspace for the thriving space cluster at Harwell Campus. Due to complete in Summer 2016, the new building has already begun to attract businesses both locally and from further afield.
The second new building, The Quad, will provide businesses with cutting edge collaborative workspaces and access to the Campus' expanding science research facilities. Harwell Campus is unrivalled in the UK for its concentration of science research and development facilities available to multinationals, academics and start-ups.
As part of Harwell Oxford Partners, we're working to accelerate the commercial development of the 700 acre science campus with a research and development focus. This will include state of the art buildings for commercial science agencies and research bodies within a new mixed-use community including several hundred homes.
For information on the workspace opportunities at Harwell Campus contact our leasing agents, Bidwells or Lambert Smith Hampton.
Harwell Campus


text:
James Hagan of Oldham, England, came to California as a sailor aboard ship and settled in Centerville, where he became a tinsmith. James married Sarah Letitia Goforth from Toronto, Canada, who came with her family to California and settled in San Lorenzo.
In the early 1880s, a serious epidemic broke out and James made hand-soldered zinc boxes, or caskets, that could be sealed for those who had died of this disease. He was advised and decided to become an undertaker and moved to San Francisco. This was about 1885. The first official record in the San Francisco Directory appeared around 1889:
"Hagan and Schofield"
James and Joseph Hagan, George W. Schofield
Undertakers and Embalmers
The 1892 San FranciscoDirectory reads:
"Hagan and Schofield Undertakers"
525 Valencia Street and 17 City Hall Avenue
In 1898, James Hagan moved to 445 Valencia Street. In 1899, James was at 13th Street near Valencia Street. He had his own stables and the family lived at the place of business. He was at this address at the time of the 1906 fire and earthquake. He rebuilt at the same site but his address was changed to 49 Duboce Avenue (13th Street was renamed Duboce Avenue after the earthquake).
James contracted with the City of San Francisco to bury the indigent. Sometimes a relative or friend would pay a small sum to have a cloth-covered casket and service. Sarah Letitia Hagan (James' wife) would cover the casket with a black cloth and tack white material inside. She was quite the seamstress and was very proud of her work. James had a carpentry shop in the basement on Duboce Avenue where he would make wooden caskets.
In 1902, James hired William Duggan, who had just been mustered out of the cavalry (Spanish-American War) at the Presidio in San Francisco. He served as a hack-driver, casket maker, gravedigger and all-around handyman. He was willing to do any work and was hired by James mainly because of his experience with horses. James owned a stable where he kept his horses and wagons for trips to the cemetery.
In 1903, Henrietta, James Hagan's youngest daughter, married William Duggan shortly after her graduation from Medical School at the University of California. James was not in favor of this marriage. William was the hired help, an Irishman and a Catholic.
William continued working in the livery business, later buying a horse and carriage and going into the taxi business, one of the first taxi services in San Francisco. When automobiles were manufactured, he bought a limousine and continued in the taxi business. Then in 1915, William studied for, and was issued, a license as an embalmer, after which he bought out William Green Undertakers in 1916.
Future entries in the San Francisco Directory included:
1916-"Duggan and O'Rielly," 1230 Valencia Street
1922- "Duggan & Carroll," 1230 Valencia Street
1923- "William Duggan & Co."
1929- "Duggan's Funeral Service," 3434 Seventeenth Street
William and Henrietta had five children: William T. and Richard J., who studied medicine, and Edwin J., Leonard F. and Letitia Duggan Welch, who worked with their father until 1959.
Letitia's son William J. Welch and family presently own and operate Duggan's Funeral Service in San Francisco. Edwin's family owns and operates Duggan's Serra Mortuary (which was started in 1963) in Daly City.
Duggan's Funeral Service Circa 1929
In 1959, Duggan's Mission Chapel was established in Sonoma. The Duggan Family, (William, his son Leonard F. and daughter, Letitia), purchased the funeral parlors from the Bisso Brothers, who had it built in 1952. The architecture is a replica of the Sonoma Mission.
The Duggan Family eventually added two small chapels and redecorated the main chapel. In 1987, Mission Cremation Service was added to the family business. It is the first and only licensed crematory in Sonoma.
Leonard F. Duggan's eldest daughter, Marilyn Duggan Caselli, managed the business until her retirement in 2016. Letitia Duggan Tatarian (Leonard's youngest daughter) retired in 2016, as well.
In 2001, Duggan's Mission Chapel purchased Bates, Evans & Fehrensen and continues to provide a high level of service as it meets the needs of the Sonoma community.
In 2004, Susan P. Duggan (Leonard's middle daughter) returned to Sonoma and re-joined the family business as a funeral director, until her retirement in 2018.
For more than 120 years, individuals entrusted their pre-need requests or wishes with the Duggan family. In 2017, Ed J. Leon purchased the business and takes pride in continuing to provide quality service to families and individuals in their time of need.


text:
from django.contrib import admin
from robot_control.models import Category, Question, Response

#### Category
class QuestionInline(admin.StackedInline):
    model = Question
    extra = 5

class CategoryAdmin(admin.ModelAdmin):
    fieldsets = [
        ('Category', {'fields': ['name', 'description', 'RFID_id']}),
    ]
    inlines = [QuestionInline]
    list_display = ('name', 'RFID_id')

admin.site.register(Category, CategoryAdmin)

#### Question
class ResponseInline(admin.TabularInline):
    model = Response
    extra = 5

class QuestionAdmin(admin.ModelAdmin):
    fieldsets = [
        ('Question', {'fields': ['title', 'explanation', 'category', 'difficulty']}),
    ]
    inlines = [ResponseInline]
    list_display = ('title', 'difficulty', 'category')
    list_filter = ['category', 'difficulty']

admin.site.register(Question, QuestionAdmin)

#### Response
class ResponseAdmin(admin.ModelAdmin):
    fieldsets = [
        ('Response', {'fields': ['body', 'true', 'question']}),
    ]
    list_display = ('body', 'true', 'question')
    list_filter = ['question']

admin.site.register(Response, ResponseAdmin)

redpajama_set_name: RedPajamaGithub