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1 S. No. 203 Advance List IN THE HIGH COURT OF JAMMU AND KASHMIR AT SRINAGAR WP (C) No. 2493/2019 CM No. 4819/2019 (Through Video Conferencing) Hotel Heaven Canal .... Petitioner Through: Mr. Shuja-ul-Haq, Advocate. v. State of J&K and others .... Respondent Through: Mr. B. A. Dar, Sr. AAG. Mr. M. A. Chashoo, AAG. CORAM: HON'BLE THE CHIEF JUSTICE HON'BLE MR. JUSTICE PUNEET GUPTA, JUDGE ORDER
09.09.2020
1. On 24th of February, 2020, we have considered this matter and recorded a detailed order which reads as follows:
"1. Petitioner, claiming to be registered partnership firm registered under Jammu & Kashmir Partnership Act vide Registration No. 3670 of 1976 dated 07.05.1976, is the owner of a building constructed over the land comprised under survey no. 396, Khewat no. 23, Khatta no. 159 in revenue estate Abi Nowpora, Tehsil Khanyar. The structure which has been constructed over the land is used by the petitioner as a hotel which was registered with the Jammu & Kashmir Tourism Department in the year 1980 and 2000.
2. The petitioner claims that the building was heavily damaged in the floods of 2014 and denied permission to repair the building. Additionally, an order dated 01.04.2019 stand issued by the J&K Pollution Control Board for immediate closure of the operation of the hotel followed by an order dated 14.05.2019 by the District Magistrate, Srinagar.
3. It is an admitted position that the Hotel stands sealed on 03.06.2019. The petitioner has received the communication dated 03.06.2019 from the Joint Director Tourism Kashmir and a notice dated 14.05.2019 from the Deputy Commissioner, Srinagar.
4. A challenge is laid by the petitioner to the aforesaid orders by way of instant writ petition.
5. When the writ petition came up for consideration before this Court on 23 rd of July, 2019, keeping in view the nature of impugned notices and the location of the building of the petitioner, it was, inter alia, directed as under:
SYED TASADUQ QADRI 2020.09.14 12:49 I attest to the accuracy and integrity of this document 2 "...5. In our view, the issue raised by the petitioner with regard to the rehabilitation of hotels which are required to be closed down because of the polluted status as reported by the State Pollution Control Board is a matter which ought to be placed before the Committee of Experts appointed by us in PIL no.159/2002. Let a copy of this writ petition be furnished to Mr. Altaf Naik, who was appointed as an amicus curiae to assist the Committee..."
6. The order dated 01.04.2019 issued by the Jammu & Kashmir Pollution Control Board (page 17 of the paper book) shows that the petitioner-hotel was set up and put up in operation in violation of the norms as envisaged under Section 25/26 of the Water (Prevention and Control of Pollution) Act, 1974 and is in the immediate vicinity of the Dal Lake, thereby adversely affecting upon the water quality of the lake. By this order, the Pollution Control Board had directed as follows:
".....Now, therefore, is exercise of powers vested in the Chairman, Jammu and Kashmir State Pollution Control Board under Section-33A of Water (Prevention and Control of Pollution) Act, 1974, immediate closure of M/s Hotel Heavan Canal Abi Nowpora, Srinagar is hereby ordered and in this behalf, the:-
Deputy Commissioner/District Magistrate Srinagar is directed to immediately close down the hotel M/s Hotel Heavan Canal Abi Nowpora, Srinagar.
Director, Tourism Kashmir with the direction to immediately revoke the registration of hotel in question.
iii. Chief Engineer, Electric & Maintenance (PDD), Kashmir with the direction to snap electric supply to the hotel immediately. iv. Mohd. Shaban Chachoo Prop. M/s Hotel Heavan Canal Abi Nowpora, Srinagar is directed to cease the operation of the hotel forthwith.
Issued with the approval of Competent Authority."
7. Pursuant to the directions made by us, the Committee of Experts on Dal Lake (PIL No. 159/2002), has accorded a hearing to the petitioner in which the Vice Chairman, LAWDA, Member Secretary, Pollution Control Board and Director Tourism Kashmir had been requested to be present. The Committee of Experts has noticed that the Hotel of the petitioner was located on the Dal Lake without any sewage treatment facility and the only sewage facility was through a soakage pit and the septic tank, both of which were ineffective. The Committee of Experts has noticed that disposal of sewage from the hotel was directly flowing into the Dal Lake.
8. So far as further recommendations and actions are concerned, the Committee of Experts has recommended as follows:
"Since the hotel is located within Dal Lake area (within the Dal waters) and has been identified for being rehabilitated, the hotel has to be closed down and shifted. Govt of J&K should formulate a policy for rehabilitation of such commercial units. The said Hotel should not be allowed to continue at the present location, even if they establish a modern sewage treatment plant and its effluent conform to the latest standards set by the pollution control board. Being located within the Dal waters, the danger of pollutants leaching into the Lake will always remain.
The Govt. of J&K may most expeditiously devise a policy for rehabilitation of commercial units since it does not have one at present. The CoE advises that the Govt. of J&K may examine the policy in this regard available under the Right to Fair Compensation and Transparency in Land Acquisition, Rehabilitation and Resettlement Act. 2013. The Policy may cover all cases that fall in the category of commercial units to avoid complaints in future and should be devised urgently.
The Petitioner is running a commercial unit that supports a number of SYED TASADUQ QADRI families. In order to support Tourism, Srinagar requires good hotels. Hence, it 2020.09.14 12:49 I attest to the accuracy and integrity of this document 3 is in the interest of the Govt and the people that whatever is possible is done for enabling such economic activities to continue but in a fair and open environment based on a transparent policy.
At the same time provision for treatment and waste should be mandatory and pollution of the Dal Lake should not be permitted. The SPCB, LAWDA and Tourism Deptt should maintain a constant vigil to identify polluters and take strong, strict and punitive action."
9. No relief can be granted to the petitioner in this writ petition.
10. It is submitted by Mr. Shuja-ul-Haq, learned counsel for the petitioner that the petitioner has no intention of running the Hotel at the location inasmuch as it adversely impacts the pollution content of the Dal Lake.
11. Learned counsel for the petitioner has contended that it is imperative that an immediate policy decision is taken by the Government of Union Territory of Jammu & Kashmir to enable the petitioner to continue with the trade and business to ensure availability of good hospitality to the tourists who visit the valley. It is further submitted that the closure of the petitioner's hotel adversely effects the livelihood not only of the petitioner but several other persons who were employed by the hotel.
12. We have observed that the Committee of Experts has noticed not only the closure of petitioner's hotel but 17 other hotels have also been closed down.
13. We also find that the matter involves a multiple departmental consultation between the Tourism Department, Pollution Control Board, LAWDA and also the Revenue Department.
14. In view thereof, let the report of the Committee of Experts and this order be placed before the Chief Secretary of the Government of J&K for examining the matter for the optimum solution of all the issues arising. It shall be open for the Chief Secretary to appoint an appropriate Committee for looking into the matter and making its recommendations keeping in view the interest of the Union Territory, the residents as well as the land / business owners and the various persons dependent for livelihood on the related businesses.
15. The Registry shall send copy of this order to the Chief Secretary as directed above."
2. On the same issue, we have also passed an order in PIL No. 159/2002, Syed Iqbal Tahir Geelani v. State of J&K and others, on 25th of August, 2020 whereby in paragraphs 22 and 23 we have directed as follows:
"22. It would appear that no specific Government Department which is looking at the work of rehabilitation. The entire exercise which the Committee of Experts is undertaking would be brought to a naught if the aspect of relocation is not efficiently and urgently addressed.
23. We, therefore, are constrained to direct that the matter shall be looked into by the Chief Secretary who shall appoint a Committee of officers and the Nodal Department which would be responsible for entire rehabilitation work. This Committee shall prepare the details of all necessary steps and the timelines within which the rehabilitation of the relocates from the Dal Lake shall be completed."
SYED TASADUQ QADRI 2020.09.14 12:49 I attest to the accuracy and integrity of this document 4
3. Mr. B. A. Dar, learned Sr. AAG, shall place an action taken report before us on the next date.
4. List on 01st of October, 2020.
(PUNEET GUPTA) (GITA MITTAL) JUDGE CHIEF JUSTICE SRINAGAR 09.09.2020 TASADUQ SAB: SYED TASADUQ QADRI 2020.09.14 12:49 I attest to the accuracy and integrity of this document


--- title: Getting Lisp layout: post tags: [] uuid: a9619303-89d6-3e8e-660b-4db0839f590b ---
I've been using lisp on and off for the past few years. I read some lisp books, went through The Little Schemer and some of SICP. But I could never really think in lisp. When I needed to write some code, I would prefer another language first. I was writing imperative code for 10 years before I saw lisp, and I was used to it.
Recently, I have found myself wanting to use lists (including alists, plists, etc.) as data structures for everything, even when I'm not even writing lisp. I think I finally got lisp and now I want to use lisp for everything.
For example, take this little problem from the other day on f(t). Katie Nowak gives us a math problem,
There is a 75% chance of rain on any given day in the next week. What is the probability that it will rain on at least 5 of the 7 days?
The purpose of the question was to point out a neat coincidence in the problem (explained at the link). I used a program to solve the problem and there are two reasons for this.
First, I wanted to use the program to explore the problem and find the "special" property. With a program, I could quickly try different parameters, which would take longer, and be more error prone, by hand.
 It's slow and error prone. Writing a program to do the same work is faster and mistakes are easier to catch. Also, I can quickly try different parameters to make sure my program's output is reasonable. In this case, for any reasonable input, the output, a probability, shouldn't be greater than 1.
Well, let's see, this is a Bernoulli experiment: each day is independent, so it is like flipping a coin seven times and counting the heads. That means we need the choose function.
My first thought was Octave, as this is a simple program and Octave
already provides nchoosek()
for me.
Simple, but I actually made a couple little mistakes working it out, and it took me a little longer than it should have. If you asked someone to write this program in any imperative language, it would probably look a lot like this.
I then made a lisp version (elisp), but I first needed to define the binomial coefficient function since there wasn't one provided.
{% highlight cl %} (defun nck (n k) "The binomial coefficient." (if (or (= k 0) (= k n)) 1 (+ (nck (- n 1) (- k 1)) (nck (- n 1) k)))) {% endhighlight %}This is the recursive version, based on Pascal's rule, so it doesn't need factorials.
In lisp, recursion is preferred to iteration, so that's the way I approached the program.
{% highlight cl %} (defun p-rain (n) "Probability of rain on exactly n days." (* (nck 7 n) (expt 0.75 n) (expt 0.25 (- 7 n)))) (defun rain-at-least (n) "Probability it will rain on at least n days." (if (> n 7) 0 (+ (p-rain n) (rain-at-least (+ 1 n))))) {% endhighlight %}I like the recursive version, and this code, much better. It presents the solution in a more straight forward way. And I got it right the first time, too. Yes, it was written after the Octave version, but I still think it counts for something.
The lisp version is also less complex. The Octave version has to use
some temporary variables, i
and sum
, which
is extra conceptual overhead. Sure, it could also be written
recursively, but this is not really the way Octave is meant to be
written.
Eh, not a great example, really. Lisp has so many powerful features, like macros (the powerful lisp kind), symbols, low-level access into the interpreter, and the REPL, that allow the programmer to do some really cool things. Many of these features are unique to lisps.
I'm really liking lisp.


new MarieSim(assembled, inputFuncopt, outputFuncopt)
The MARIE simulator.
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
assembled |
object | The assembled program produced by MarieAsm. | |
inputFunc |
function |
<optional> |
Handle input function calls. |
outputFunc |
function |
<optional> |
Handle output function calls. |
Throws:
MarieSimError
Members
operators
A dictionary of operators which each
provides the opcode, whether an operand is needed or not, and the
generator function which each function call executes one
micro-instruction.
Methods
current() → {Object}
Returns the current instruction from memory, according to the value of
the PC register (not necessarily the current instruction the simulator
is processing).
Returns:
A memory location containing its contents (and the
corresponding program instruction if it exists).
- Type
- Object
decode()
Performs the decode part of the
fetch-decode-execute cycle.
Throws:
MarieSimError
execute()
Performs the execute part of the
fetch-decode-execute cycle.
Note: this is a generator function.
fetch()
Performs the fetch part of the
fetch-decode-execute cycle.
Note: this is a generator function.
microStep()
Performs one RTL operation
(or decoding the instruction).
regAdd(target, source, alu_type)
Performs a RTL operation that adds/subtracts
the source to/from the target, and stores it in the target.
Parameters:
| Name | Type | Description |
|---|---|---|
target |
String | final place to store computed value |
source |
String | Number | where to read value from |
alu_type |
String | perform specified operation |
regSet(target, source, maskopt, alu_typeopt)
Performs a RTL operation that sets the target the value
of the source.
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
target |
String | The final place to store value. | |
source |
String | Number | Where to read value from? | |
mask |
Number |
<optional> |
Bit masking that allows certain bits to pass through value. |
alu_type |
String |
<optional> |
Set ALU operation type. |
restart()
Restarts the simulator.
Resets PC register to the origin of the assembled
program, and sets all other registers to zero. It
also resets halted and paused state to false, and
sets stepper to null and empties the stateHistory
array.
run()
Runs the simulator machine.
Note: This method blocks until machine execution completes.
setEventListener(event, callbackopt)
Set the event listener to call function once
the event is fired.
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
event |
string | Which event to set callback function to. | |
callback |
function |
<optional> |
Function to call back once event fires. If not specified (or is a falsy value), then the event firing will not call any function. |
step()
Fetches and decodes only one instruction per
step.
toJSON() → {Object}
Serialises the current state of MARIE machine into a
JSON object.
Returns:
Serialised state of MARIE machine.
- Type
- Object


7 Confounding and deconfounding
7.1 Confounding and experimental effects
Common cause confounder is one of the main reasons of why correlation doesn’t imply causation.
Suppose we want to measure the causal effect of changing \(X\) on \(Y\). Confounder is a variable that causes both \(X\) and \(Y\). Thus, it enables a spourious path of causation from \(X\) to \(Y\) even if there is no direct edge from \(X\) to \(Y\). Known confounders are often called covariates in Rubin’s causal model.
- Example 1: Fire escape!
- Suppose we want to know the effect someone screaming “fire” in a movie theater has on making people run for the fire exit.
- But when people usually scream fire, there is usually a fire. When people run to the exit, are they responding to the scream or to the fire?
Here, we are interested in a direct relationship between (screaming and running), and are having troubling seperating it from indirect relationship between screaming and the presence of a fire, as well as people running because of the presence of a fire.
- Example 2: A/B test
- Suppose you run an A/B test to figure out the effect \(Y\), where \(X\) is the group a or b. There is an unaccounted variable, say, \(Z\), that is age.
- The question is, how much Y differs for group A vs group B? \[\begin{align*} & p(Y = 1 \mid X = a) - p(Y = 1 \mid X = b)\\ =& \sum_{z}p(Y = 1 \mid X = a, Z=z)p(X=a\mid Z=z)P(Z=z)\ -\\& \sum_{z}p(Y = 1 \mid X = b, Z=z)P(X=b\mid Z=z)P(Z=z) \end{align*}\]
- This is an incorrect measure, because the probabilities depend on the level of Z. For example, if \(p(Y=1 \mid Z = \text{'below 18'})\) is higher than \(p(Y=1 \mid Z = \text{'above 18'})\) and most of the people in group A is below 18, then age will have a huge impact the final quantity that we calculated above. This is often called confounding bias. In other words, for every level of Z, the relationship between X and Y may be different.
- This is why we need to intervene on the cause variable. If we correctly intervene on a variable, we get rid of all the incoming edges, thus we get rid of all the spourious causal effects because of common-cause confounders.
Confounding bias
Confounding bias occurs when a variable influences both who is selected for the treatment and the outcome of an experiment. Sometimes they are known, sometimes they are latent.
It is important not to confuse causal models with latent confounders with latent probabilistic models like mixture models or LDA. There specify what latent variable is, and infer them. For example, cluster assignment in Gaussian mixture model.
In causal inference, we want to use techniques that compute the direct causal effects between the variables of our interests.
We now lay out the techniques for accounting for known as well as latent confounders in this lecture. If the confounder is known, we control for it. This process is also called deconfounding or adjusting. We will use these words interchangably, but they’re the same.
The cool thing about deconfounding is, we don’t need do-calculus or DAGs to derive to an intervention distribution. This is how interventions are performed in randomized trials.
7.2 Calculating intervention distributions by covariate adjustment / deconfounding
Deconfounding is the way to remove the effect of known confounders. In Rubin’s causal model, known confunders are often called covariates, thus, the term covariate adjustment. As mentioned above, we don’t need do-calculus to get the intervention distribution. Let’s demonstrate it with two cases.
7.2.1 1. X does not have parents
We know that causal mechanisams (factors) are independent. So, causal relationships remain autonomous under intervention. That means, if we intervene on a variable (or we mutilate the graph), then the other mechanisms remain invariant. This is an important property that lets us compare relationships between variables before and after graph mutilation.
From the local markov property, we know that
\[P^{\mathbb{M}}(X_1,X_2,...,X_D) = \prod_{i=1}^D P^{\mathbb{M}}(X_i \mid PA_i)\] Let’s intervene on \(X_1\). \[\widehat{\mathbb{M}} = \mathbb{M}^{do(X_1=N)}\] where \(N \sim \pi(x)\). \(\pi\) is direc-delta distribution. Direc-delta can be derived from taking limit on the Normal distribution. \[\pi(\mu) = \lim_{\sigma\to 0}\mathcal{N}(\mu,\sigma^2)\]
In other words, all mass is concentrated on \(\mu\). The PMF of a direc-delta distribution is \[\pi(\mu) = \begin{cases}1\text{, if }\mu\\0\text{, Otherwise}\end{cases}\]
Getting back to the joint distribution post intervention, \[P^{\widehat{\mathbb{M}}}(X_1,X_2,...,X_D) = \pi(x_1)\prod_{i=2}^D P^{\mathbb{M}}(X_i \mid PA_i)\]
\[P^{\widehat{\mathbb{M}}}(X_1,X_2,...,X_D)= \begin{cases} \prod_{i=2}^D P^{\mathbb{M}}(X_i \mid PA_i)\text{ , if } X_1=x_1\\ 0\text{ , Otherwise} \end{cases}\]
But, we know that \(X_1\) does not have parents. Thus, \(P(X_1\mid PA_1) = P(X_1)\)
\[ \begin{align} P^{\mathbb{M}}(X_1,X_2,...,X_D) &= \prod_{i=1}^D P^{\mathbb{M}}(X_i \mid PA_i)\\ &= P(X_1)\prod_{i=2}^D P^{\mathbb{M}}(X_i \mid PA_i) \end{align}\]
\[P^{\mathbb{M}}(X_2,X_3,...,X_D\mid X_1 = x_1) = \frac{P(X_1=x_1)\prod_{i=2}^D P^{\mathbb{M}}(X_i \mid PA_i)}{P(X_1 = x_1)}\text{ (Conditional probability)}\]
\[P^{\mathbb{M}}(X_1,X_2,...,X_D\mid X_1=x_1)= \begin{cases} \prod_{i=2}^D P^{\mathbb{M}}(X_i \mid PA_i)\text{ , If }X_1=x_1\\ 0\text{ , Otherwise} \end{cases}\]
Conditioning on the variable that does not have parents is the same as doing an intervention.
7.2.2 2. X has parents
Let \(Z\) be the parent of \(X\) and \(Y\). Note that \(Z\) can be a set of confounders that cause both \(X\) and \(Y\).
The factorization for the DAG above is \[ \begin{align} P^{\mathbb{M}}(X,Y,Z)&= P(Y\mid Z, X)P(X\mid Z)P(Z) \end{align} \]
Let the mutilated model \(\widehat{\mathbb{M}}\) be the intervention model \(\mathbb{M}^{do(X=x)}\). We again use direc delta distribution to represent the intervention
\[ \begin{align} P^{\widehat{\mathbb{M}}}(Y, Z\mid do(X=x))&= P(Y\mid Z,X=x)\pi(x)P(Z)\\ &= P(Y\mid Z,X=x)P(Z) \end{align} \]
All we have to do in order to get the effect of intervention on Y is to marginalize \(Z\) from the above equation.
\[ \begin{align} P^{\widehat{\mathbb{M}}}(Y, Z\mid do(X=x)) &= P(Y\mid Z,X=x)P(Z)\\ P^{\widehat{\mathbb{M}}}(Y\mid do(X=x)) &= \sum_{z}P(Y\mid X=x, Z=z)P(Z=z)\\ \end{align} \]
This is one of the most important formula in causal inference. Note that there is no do statement on the R.H.S. This means that we can derive causal effect by just using conditionals (statistical quantity). This is known as adjustment formula or Deconfounding.
In words, we control (or adjust) for \(Z\) while calculating the effect of change in \(X\) on \(Y\).
7.2.3 Continuous adjustment
Perhaps we want to know about the effect of a treatment in the continous case. By looking at the expectation of the distribution and taking the derivative with respect to the treatment variable, we can get an idea of how the treatment affects results. \(\frac{\partial}{\partial x} \mathbb{E}^{do(X=x)}[Y]\)
In the case of a linear Gaussian system, the above function is a constant. Given a valid adjustment set \(\mathbf{Z}\), then \(p(Y|X=x,\mathbf{Z}=\mathbf{z})\) is a Gaussian distribution with mean:
\(\mathbb{E}[Y|X=x,\mathbf{Z}=\mathbf{z}] = ax+ \mathbf{b}^t \mathbf{z}\)
Thus, taking the derivative results in: \(\frac{\partial}{\partial x} \mathbb{E}^{do(X=x)}[Y] = a\)
To find the value for \(a\), it is common to use samples from the joint distribution to regress \(Y\) on \(X\) and \(\mathbf{Z}\) and taking the coefficient associated with \(X\).
7.3 Criterions for valid adjustment sets
As you might have guessed, it’s not a good idea to adjust for everything we have in our model. Just like d-separation provides graphical intuition of conditional independence, these criterions below provides a graphical intuition about what we should adjust for.
7.3.1 Back door criterion\(^{[2]}\)
Given an ordered pair of variables \((X,Y)\) in a DAG \(\mathbb{G}\), a set of variables \(Z\) satisfies backdoor criterion relative to \((X, Y)\) if no node in \(Z\) is a descendant of \(X\), and \(Z\) blocks every path between \(X\) and \(Y\) that contains an arrow into \(X\).
If \(Z\) satisfies backdoor criterion for \((X,Y)\), then causal effect of \(X\) on \(Y\) is given by this formula
\[P(Y=y\mid do(X=x)) = \sum_z P(Y=y\mid X=x,Z=z)P(Z=z)\]
Example 1.
Let us consider the above graph. Only the path X<-A->K->Y is a backdoor path from X to Y. The set \(Z=\{K\}\) statisfies the bacdoor criterion, thus it is a valid adjustment set. Note that \(Z=\{F,C,K\}\) is also a valid adjustment set for \((X,Y)\).
7.3.2 Front door criterion\(^{[2]}\)
We saw how to adjust for known confounders using backdoor criterion. Often times, we have to deal with latent confounders. For example, consider a century long debate on relation between smoking and lung cancer.
Fig 3.10 (a) does not satisfy the backdoor condition because variable \(U\) is unobserved and hence cannot be used to block the backdoor path from \(X\) to \(Y\). The causal effect of smoking on lung cancer is not identifiable in this model.
However, it turns out that we can go much further by considering the model in 3.10(b), where an additional measurement is available. - the amount of tar deposits in patient. Though this model still does not satisfy backdoor criterion, because there is still no variable capable of blocking the spourious path \(X \gets U \to Y\). We will see, however, the causal effect \(P(Y=y\mid do(X=x))\) is identifiable, through two consecutive application of backdoor criterion.
Observe that since Y is a collidor (v-structure), there is no backdoor path from \(X\) to \(Z\). Thus, we can write
\[P(Z=z\mid do(X=x)) = P(Z=z\mid X=x)\]
Next, we note that the effect of \(Z\) on \(Y\) is also identifiable, since the backdoor path from \(Z\) to \(Y\), namely \(Z \gets X \gets U \to Y\), can be blocked by adjusting for X. Thus, we can write
\[P(Y=y\mid do(Z=z)) = \sum_{x'}P(Y=y\mid Z=z,X=x')\]
Both formulas are derived from backdoor criterion. First is by adjusting for null set, and the second by adjusting for \(X\).
We can now chain together the two partial effects to obtain the overall effect of \(X\) on \(Y\). The reasoning goes as follows: If nature chooses to assign \(Z\) with value \(z\), then the probability of \(Y\) would be \(P(Y=y\mid do(Z=z))\). But the probability of nature would choose to do that, given that we choose to set \(X\) as \(x\), is \(P(Z=z\mid do(X=x))\). Therefore,
\[ \begin{align} P(Y=y\mid do(X=x)) &= \sum_zP(Y=y\mid do(Z=z))P(Z=z\mid do(X=x))\\ &= \sum_z\sum_{x'}P(Y=y\mid Z=z,X=x')P(X=x')P(Z=z\mid X=x)\\ &= \sum_{z}P(Z=z\mid X=x)\sum_{x'}P(Y=y\mid X=x',Z=z)P(X=x') \end{align} \]
Front door criterion
A set of variables \(Z\) satisfies the front-door criterion relative to an ordered pair of variables \((X, Y)\) if
- Z intercepts all directed paths from X to Y
- There are no unblocked paths from X to Z
- All backdoor paths from Z to Y are blocked by X
If \(P(x,z)>0\), then the causal effect of \(X\) on \(Y\) is identifiable and is given by formula \[P(Y=y\mid do(X=x)) = \sum_{z}P(Z=z\mid X=x)\sum_{x'}P(Y=y\mid X=x',Z=z)P(X=x')\]
Example
To develop familiarity with the concept and understand better where the formula comes from, let us consider an example.  You assume the following DAG where a users context (\(U\)) is a confounder on the causal effects of social media use (\(X\)) on conversion (\(Y\)).
Unfortunately, you do not have access to data on all the factors contained in a user’s context that might influence conversion. However, as you run more SQL queries, you find information on users’ use of ad blocking software. With this additional variable, the DAG changes as shown below. Excitedly, you notice that use of ad block (variable \(Z\)) satisfies the front door criterion relative to the causal effect of social media use on conversion so you can use front door adjustment.
The query you performed generated data from a past experiment on a random sample of 800k “whales” (tech lingo for a user who is known to have a high conversion, as in spends a lot of money on purchases). The data from the query is condensed in the two tables below.
<IP_ADDRESS> Table 1
| _ | No Ad Block 400 | Ad Block 400 | All subjects 800 | |||
|---|---|---|---|---|---|---|
| Social | No Social | Social | No Social | Social | No Social | |
| total | 380 | 20 | 20 | 380 | 400 | 400 |
| No Click | 323 (85%) | 1 (5%) | 18 (90%) | 38 (10%) | 341 (85%) | 39 (9.75%) |
| Click | 57 (15%) | 19 (95%) | 2 (2%) | 342 (90%) | 59 (15% | |
| ) | 361 (92.25%) |
<IP_ADDRESS> Table 2
| _ | Social 400 | No Social 400 | All subjects 800 | |||
|---|---|---|---|---|---|---|
| No Ad Block | Ad Block | No Ad Block | Ad Block | No Ad Block | Ad Block | |
| total | 380 | 20 | 20 | 380 | 400 | 400 |
| No Click | 323 (85%) | 18 (90%) | 1 (5%) | 38 (10%) | 324 (81%) | 56 (19%) |
| Click | 57 (15%) | 2 (10%) | 19 (95%) | 342 (90%) | 76 (9%) | 344 (81%) |
When you show the tables to some coworkers, a disagreement arises between two of them. One argues that social media use does not cause conversion. To make their point, they repeat that only 15% of people who converted used social media whereas 92.25% of people who do not use social media. On the other side of the argument, a co-worker points out that social media use increases the likelihood that you will see ads (i.e. no ad block). This counter argument uses Table 2 to claim that seeing more ads causes higher conversion, thus social media use results in better conversion rates.
You decide to use your knowledge of causal inference to find the right answer.
- The effect of \(X\) on \(Z\) is identifiable because there is no backdoor path from \(Z\) to \(X\). In other words, \(p^{do(X=x)}(Z=z) = p(Z=z | X=x)\).
- You note that the effect of \(Z\) on \(Y\) is identifiable. The back door path from \(Z\) to \(Y\) (i.e. \(Z \leftarrow X \leftarrow U \rightarrow Y\)) can be adjusted for by conditioning on \(X\) as such: \(p^{do(Z=z)}(Y=y) = \sum\limits_{x} p (Y=y|Z=z, X=x)\,p (X=x)\)
- You chain the two partial effects together to obtain the overall effect of \(X\) on \(Y\).
- If nature chooses to assign \(Z\) the value \(z\), then the probability of \(Y\) would be \(p^{do(Z=z)}(Y=y)\)
- The probability that nature would choose to do that, given that we choose to set \(X\) to \(x\) is \(p^{do(X=x)}(Z=z)\)
- Summing up over all the possible values \(z\) of \(Z\), we have \(p^{do(X=x)}(Y=y) = \sum\limits_{z} p^{do(Z=z)} (Y=y)\, p^{do(X=x)}(Z=z)\)
- You replace all the do-expressions with the covariate adjustment counterparts. The final expression is \(p^{do(X=x)}(Y=y) = \sum\limits_{z} \sum\limits_{x'} p (Y=y|Z=z, X=x')\,p (X=x')\,p(Z=z|X=x)\)
The reader will notice that the final expression is exactly the same as the formula provided above. The exact numerical result after substituting in values from the tables is left to the reader, as it will be performed in a future homework assignment.
7.4 References
The do-calculus - Stanford encyclopedia of philosophy - https://plato.stanford.edu/entries/causal-models/do-calculus.html
Causal inference in statistics - a primer, Judea Pearl.
6.1, 6.5 of Peters, Jonas, Dominik Janzing, and Bernhard Schölkopf. Elements of causal inference: foundations and learning algorithms. MIT press, 2017.
Lecture Notes and Notes uploaded by Professor Robert Ness


This website is about finding you story preference if you don't know what genra your into. This is a free trial based website to ready as many stories as you please. When you are finished reading you may comment at the bottom of the screen you thoughts and feedback. If you are having am issue please click the issues link above at state you complaint. Thank you so such for choosing your website please come again soon. Happy Story Time.
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Communication over RexPro is achieved by sending and receiving messages that adhere to the format specified on this page. There are several clients that communicate via RexPro that would serve as good examples for how communication over RexPro works:
Every RexPro message consists of the following segments:
[protocol version][message type][message size][message body]
| segment | type(bytes) | description |
|---|---|---|
| protocol version | byte(1) |
The version of RexPro. Currently, this value should be set to zero. |
| message type | byte(1) |
The type of message as described below. |
| message size | int(4) |
The length of the message body |
| message body | byte(n) |
The body of the message itself, containing the instructions for, or a response from, the RexPro Server. This portion of the message is an array of message fields serialized using MsgPack. The fields for each specific message are described below. |
RexPro messages are divided into those that deal with requests made from the client and those that involved a response from the server. The following table represents an overview of the basic message set:
| message | type | description |
|---|---|---|
| session | request | A request to open or close the session with the RexPro Server. |
| session | response | The response from the RexPro Server to a session request containing session information. |
| script | request | A request to process a Gremlin script on the RexPro Server. |
| script | response | A response to a script request. Script response messages actually refer to a set of response messages that are unique to each serialization channel (i.e. MsgPack, GraphSON, and Console). |
| error | response | A response from the RexPro Server indicating a problem has occurred. |
NOTE – The following sections describe the individual RexPro Messages in greater detail. It is important to note that the messages are defined in terms of MsgPack serialization, so data types defined must adhere to MsgPack expectations. The order of fields in the sections below are important to adhering to the format. They should be serialized to a MsgPack array.
The request class of messages are those that are sent by the client to the RexPro Server for processing.
The Session Request Message is used to establish or destroy a session on the RexPro Server. When a session is opened, future Script Request Messages can be executed within this session and variable bindings established in previous scripts can be accessed.
| field | type | description |
|---|---|---|
Session |
byte(16) |
The UUID for the Session. Set each byte to zero for an “empty” session. |
Request |
byte(16) |
The UUID for the request. Uniquely identifies the request from the client. This value gets passed back from the RexPro Server so that the response message can be mapped to a request if necessary. |
Meta |
Map |
Message specific properties described below. |
Channel |
Integer |
Defines the channel of serialization for the life of the session. The channel cannot be changed on future requests to the session. |
Username |
String |
The username to access the RexPro Server assuming Rexster authentication is turned on. The field is ignored in cases where it is not enabled. |
Password |
String |
The password to access the RexPro Server assuming Rexster authentication is turned on. The field is ignored in cases where it is not enabled. |
| field | type | description |
|---|---|---|
graphName |
String |
The name of the graph to open a session on. Optional. |
graphObjName |
String |
The variable name of the graph object, defaults to g . Optional. |
killSession |
Boolean |
If true, the given session will be destroyed, otherwise, one will be created, defaults to false. |
The Script Request Message is used to send Gremlin scripts to the RexPro Server. Script Requests can be made either in-session or sessionless. In-session requires that a session is already established with the RexPro Server via the Session Request Message. Sessionless Script Request Messages can be sent without the context of the session but come with the limitation that variable bindings established by a script are lost between requests.
| field | type | description |
|---|---|---|
Session |
byte(16) |
The UUID for the Session. Set each byte to zero for an “empty” session. |
Request |
byte(16) |
The UUID for the request. Uniquely identifies the request from the client. This value gets passed back from the RexPro Server so that the response message can be mapped to a request if necessary. |
Meta |
Map |
Message specific properties described below. |
LanguageName |
String |
The Gremlin flavor to request the RexPro Server to process the message with. This value is dependent upon the configured script engine languages in rexster.xml . Generally speaking, this value should be set to groovy . |
Script |
String |
The Gremlin script to execute on the server. |
Bindings |
Map |
Bindings are arguments passed with the script that become variables to the Script Engine when the script is executed. Use bindings to parameterize scripts. Bindings must be serializable by MsgPack. |
| field | type | description |
|---|---|---|
channel |
Integer |
Used where inSession is false and the channel of communication is established for each request. If this value is used when inSession is true it must match the value established by the session. |
inSession |
Boolean |
Indicates this requests should be executed in the supplied session. Defaults to false . Optional. |
isolate |
Boolean |
Bindings from previous messages are not available in subsequent requests. Defaults to true . Optional. |
transaction |
Boolean |
Executes script within a transaction, committing if successful, rolling back if not. Has no effect with non transactional graph. Defaults to true . Optional. |
graphName |
String |
The name of the graph to open a session on. Optional. |
graphObjName |
String |
The variable name of the graph object. Defaults to g . Optional. |
NOTE – supplying graphName
or graphObjName
for a session request which already has these values defined will raise an exception.
The response class of messages are those that are sent by the RexPro Server back to the client, in response to a request message.
The Session Response Message is used to send a response to a Session Request Message and is sent when the session is successfully created. The Session Response Message will contain information about the session created by the RexPro Server.
| field | type | description |
|---|---|---|
Session |
byte(16) |
The UUID for the Session. Used on future Script Request Messages that need to be made on the session. |
Request |
byte(16) |
The UUID for the request. Uniquely identifies the request from the client. This value gets passed back from the RexPro Server so that the response message can be mapped to a request if necessary. |
Meta |
Map |
Not applicable for this message |
Languages |
String[] |
The list of Gremlin flavors that are configured for Rexster. By default the language list will just be groovy . |
The Console Script Response Message contains the results of a Gremlin script sent in a Script Request Message, where the results are serialized into strings that are best displayed in a console or terminal. Rexster Console uses this message for communication with the RexPro Server.
| field | type | description |
|---|---|---|
Session |
byte(16) |
The UUID for the Session. Used on future Script Request Messages that need to be made on the session. |
Request |
byte(16) |
The UUID for the request. Uniquely identifies the request from the client. This value gets passed back from the RexPro Server so that the response message can be mapped to a request if necessary. |
Meta |
Map |
Not applicable for this message |
ConsoleLines |
String[] |
List of results serialized to strings where each string is a line that should be printed to a console output. |
Bindings |
Map |
Once the script is executed any bindings on the Script Engine that can be serialized via MsgPack are returned. Any that cannot be properly serialized are converted via toString . |
The GraphSON Script Response Message contains the results of a Gremlin script sent in a Script Request Message, where the results are serialized into a JSON string by the GraphSON converter from Blueprints.
| field | type | description |
|---|---|---|
Session |
byte(16) |
The UUID for the Session. Used on future Script Request Messages that need to be made on the session. |
Request |
byte(16) |
The UUID for the request. Uniquely identifies the request from the client. This value gets passed back from the RexPro Server so that the response message can be mapped to a request if necessary. |
Meta |
Map |
Not applicable for this message |
Results |
String |
The result serialized to a JSON string. |
Bindings |
Map |
Once the script is executed any bindings on the Script Engine that can be serialized via MsgPack are returned. Any that cannot be properly serialized are converted via toString . |
The MsgPack Script Response Message contains the results of a Gremlin script sent in a Script Request Message, where the results are serialized by MsgPack.
| field | type | description |
|---|---|---|
Session |
byte(16) |
The UUID for the Session. Used on future Script Request Messages that need to be made on the session. |
Request |
byte(16) |
The UUID for the request. Uniquely identifies the request from the client. This value gets passed back from the RexPro Server so that the response message can be mapped to a request if necessary. |
Meta |
Map |
Not applicable for this message |
Results |
Object |
The result serialized by MsgPack. Any result not serializable by MsgPack is converted via toString . |
Bindings |
Map |
Once the script is executed any bindings on the Script Engine that can be serialized via MsgPack are returned. Any that cannot be properly serialized are converted via toString . |
An Error Response Message may be returned by the RexPro Server for any request message sent.
| field | type | description |
|---|---|---|
Session |
byte(16) |
The UUID for the Session. Used on future Script Request Messages that need to be made on the session. |
Request |
byte(16) |
The UUID for the request. Uniquely identifies the request from the client. This value gets passed back from the RexPro Server so that the response message can be mapped to a request if necessary. |
Meta |
Map |
Not applicable for this message |
ErrorMessage |
String |
The error message from the server. |
| field | type | description |
|---|---|---|
flag |
Integer |
– 0 : The message sent to the RexPro Server was malformed.– 1 : A session was requested that has either expired or no longer exists.– 2 : A script failed to execute (likely cause is syntax error).– 3 : Invalid username/password if authentication is turned on.– 4 : A graph requested via graphName meta attribute did not exist.– 5 : The channel requested did not exist or the channel was changed after being established on the session. – 6 : The result or an item in the bindings could not be serialized properly. |


--- layout: default ---
Snippets, scripts and neat commands.
Go get some!
{{ link.title }} added on {{ link.date | date: "%B %e, %Y" }} {% if link.author %} by {{ link.author }}{% endif %} - {{ link.description }}


Enter some sequences to align.
Optionally fiddle with scoring.
First, a "1" value is shown in all cells with matching letters.
Second, the matrix is "walked" from bottom-right to top-left.
Third, we trace the best scores from top-left to bottom-right.
Lastly, we print the best alignment!


Review: PC Building Simulator
Interview with Leigh Milner & Chris Berrow, Naked Gaming Podcast
Part of the show Simulator Special 2
Leigh Milner & Chris Berrow play PC Building Simulator!
Leigh - Okay so in someone's office it's like my old bedroom gets my e-mails email. Yeah. Cause like being back at work. Thanks for taking over the P.C. shop for me. Sorry it's not the best day. Few things come in. Companies aren't doing very well. There's no money in the bank account. The rents and energy bill. Is this you sent this so these emails are going very badly for this company. I had the job and the guy we should help with the rent see email below. Flip it back is like being at work. Got to pick up a parcel. Well I don't want that I don't want to read it. I want to blow something up or raise something I'm currently picking up a parcel I'm walking round an office block.
Chris - You're on minus 15 dollars. You need this job. I've done my best effort. Did you need the computer. Lee Milner. Is going to go down the path of paper to a kitten and the puppy and I plugged it in and turned it on.
Leigh - Well let's say you are if you help me out in the shop I'll give you 10 percent. If I ever get bored of journalism or presenting I will definitely give PC Building Simulator a go!


This sketch classify what you draw on the canvas and guess the drawing using a neural network library
Train and test buttons are used for neural network pupose
Guess button is used for test your draw
Clear button is for resetting the canvas
Data available for drawing: cats, rainbows, trains, dogs, horses, houses, plants
Open the console for more details on what's going on


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--- layout: post title: '30 Days in April: Day 24' date: 2009-04-24 23:45:46.000000000 -07:00 type: post published: true status: publish categories: - 30 Days in April tags: - 30days - cameraphone - i wish i had more time - lazy - photo meta: _publicize_pending: '1' _edit_last: '6192' aktt_tweeted: '1' original_post_id: '274' _wp_old_slug: '274' author: login: falsefridays email<EMAIL_ADDRESS>display_name: Jim Withington first_name: '' last_name: '' ---
Today's art brought to you by the letter tired and the number queasy.
I like cameraphone shots also.


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UK parties woo veterans on Armistice Day in election push
by The Associated PressLONDON (AP) — Britain’s main political parties are using Armistice Day to showcase plans to improve the lot of veterans if they win the Dec. 12 general election.
Prime Minister Boris Johnson’s party Monday unveiled proposals to make it harder to bring veterans to court over allegations of abuse that took place before the Human Rights Act took force in 2000, and a series of measures to help veterans get jobs.
Jeremy Corbyn’s Labour Party said it would improve housing and work conditions for veterans.
Also on Monday, Corbyn’s foreign policy adviser, Emily Thornberry, raised questions about Britain’s nuclear deterrent when she told ITV it is not clear Corbyn as prime minister would use nuclear weapons if Britain is threatened.

The Associated Press


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class: center, middle, inverse, title-slide # Touring tidyverse ## dplyr ### Misha Balyasin ### 2018/05/24 --- background-image: url(http://r4ds.had.co.nz/diagrams/data-science.png) background-size: 800px --- background-image: url(https://www.rstudio.com/wp-content/uploads/2015/01/dplyr-hexbin-logo.png) background-size: 100px background-position: 90% 8% # dplyr has three main goals: 1. Identify the most important data manipulation verbs and make them easy to use from R. 2. Provide blazing fast performance for in-memory data by writing key pieces in C++ (using Rcpp) 3. Use the same interface to work with data no matter where it's stored, whether in a data frame, a data table or database. --- # Initial commit of dplyr The year was 2012... (`2012-10-28 19:37` to be precise) ``` Package: plyr2 Type: Package Title: Tools for splitting, applying and combining data Version: 0.01 Author: Hadley Wickham<EMAIL_ADDRESS>Maintainer: Hadley Wickham<EMAIL_ADDRESS>Description: ddply on crack Depends: R (>= 2.15.1) License: MIT ``` --- # 23 minutes later ``` Package: dplyr Type: Package Title: dplyr: a grammar of data manipulation Version: 0.01 Author: Hadley Wickham<EMAIL_ADDRESS>Maintainer: Hadley Wickham<EMAIL_ADDRESS>Description: A fast, consistent tools for working with data frame like objects, both in memory and out of memory. Imports: stringr Depends: R (>= 2.15.1) License: MIT ``` --- # Current state 1. `dplyr` replaced `plyr` to specialize on data frames. 2. Current version - 0.7.6. 3. https://github.com/tidyverse/dplyr 4. Developed by __Hadley Wickham__, Romain François, Lionel Henry, Kirill Müller. 5. 5000+ commits by 151 contributors. --- background-image: url(https://www.rstudio.com/wp-content/uploads/2015/01/dplyr-hexbin-logo.png) background-size: 100px background-position: 90% 8% # dplyr 5 verbs: * `mutate()` adds new variables that are functions of existing variables * `select()` picks variables based on their names. * `filter()` picks cases based on their values. * `summarise()` reduces multiple values down to a single summary. * `arrange()` changes the ordering of the rows. --- # Overview of the API .pull-left[ 1. Single-table verbs. 2. Single-table helpers. 3. Two-table verbs. 4. Remote tables. 5. Vector functions. ``` ## [1] 215 ``` ] -- .pull-right[ 1. Selecting columns. 2. Transforming columns. 3. Filtering rows. 4. Summarizing and slicing. 5. `dbplyr`. Based on https://github.com/suzanbaert/RTutorials ] --- # Not covered 1. Joins (`inner_join()`, `left_join()` etc.). 2. `group_by_*()`. 3. Binding (`bind_rows()`, `bind_cols()`, `combine()`). 4. Cumulative functions (`cumall()`, `cumany()` etc.). 5. Windowing functions (`?ranking` for the full list). --- # Selecting columns Notable participants: * `select()` * `rename()` * `select_*()` * `first()`, `last()`, `nth()` * `everything()` --- # Transforming columns Notable participants: * `mutate()` * `mutate_*()` * `na_if()` * `case_when()` * `recode()` and `recode_factor()` --- # Filtering rows Notable partiicipants: * `filter()` * `filter_*()` * `near()`, `between()` * `any_vars()` and `all_vars()` --- # Summarizing and slicing Notable partiicipants: * `summarize()` and `summarize_*()` * `add_tally()` and `add_count()` * `arrange()` * `group_by()` * `top_n()`, `sample_frac()`, `sample_n()`, `slice()` * `do()` --- # `dbplyr` Notable partiicipants: * `collect()` * `explain()` * `compute()` * `DBI` and `odbc` packages --- # Alternatives * `data.table` > data.table builds on base R functionality to reduce 2 types of time: programming time (easier to write, read, debug and maintain), and compute time (fast and memory efficient). * [`rquery`](https://github.com/WinVector/rquery) + [`rqdatatable`](https://github.com/WinVector/rqdatatable) * others? --- # Resources 1. https://dplyr.tidyverse.org/index.html 2. http://r4ds.had.co.nz/transform.html 3. https://suzan.rbind.io/2018/01/dplyr-tutorial-1/ 4. https://dplyr.tidyverse.org/reference/index.html 5. https://github.com/rstudio/cheatsheets/blob/master/data-transformation.pdf 6. https://www.nielsenmark.us/2018/07/07/connecting-r-to-postgresql-on-linux/ 7. http://db.rstudio.com/ --- # Contacts http://mishabalyasin.com/ Slides, markdown and code - https://github.com/romatik/touring_the_tidyverse


Mushrooms - P. tampanesis
It all began when a friend invited me and M for a day out to Alton Towers, A British Theme Park. I suggested that a dose of magic mushrooms may increase the enjoyment of the day out so the day before we went to our local head shop and purchased 15 grammes of philosophers stones each as the only mushrooms available were strains which we had either not tried or had unpleasant trips with. Being conscientious little trip heads we also bought some vitamins (B complex and C) and Paracetamol so that we could hasten a come down if we began to bad trip. We looked forward to an early night so we would be fully refreshed in the morning.
We both awoke at around 7am and began to ready ourselves for the day out, showers, good breakfast, etc. By 8am our friend C and her boyfriend P arrived to pick us up for the journey to the park. We arrived at approximately 10am, parked up the car and took the monorail to the parkís main entrance. By now we are both very excited about the trip ahead of us, the car ride had given us a chance to relax into the day and bond with C and P who would essentially be our sitters while we tripped. To get to know this new unfamiliar surrounding we let ourselves take a few rides to get a feeling for the day ahead. After riding the Oblivion (vertical drop rollercoaster) we both began to feel a little uneasy about what we were about to embark on. We took some time to make a final decision and then at about 12pm we mixed the stones in with a yogurt and spooned the unassuming truffles into our eagerly awaiting bodies.
The next ride was Submission, a pirate ship type ride that takes you 360 degrees around. M was feeling very apprehensive about what we had just done but I felt very excited and let myself relax. After the ride M felt a lot more comfortable and we walked towards the next ride, Enterprise, a big wheel type ride. By the time that was over we had both begun to feel the stones work their magic. I felt slightly disorientated and giggly. M was in a heightened sense of well being. We crossed the park to the Corkscrew, a rollercoaster. M got a serious case of the giggles as we ascended to the first peak of the ride, the view was stunning, and we wanted to stay there forever. We laughed our asses off for the rest of the ride.
Walking to the next ride I am feeling disorientated, I donít really know exactly where I am going, good thing C and P where there to guide us, although M does not feel like he is really coming up and has accepted that he may just have to settle for a light buzz and just try and enjoy the rides. We had to take a skyline over to the next section of rides and we just laughed and talked trippy bollocks. There were quite a few people in with us who seemed confused as to our state of being; M is acting it up because he enjoys the paranoia. I however feel apprehensive about the situation; sober people have never been my strong point when tripping the light fantastic.
Despite our lack of time perception I believe it was about 1pm when we boarded the Nemesis, another rollercoaster and my favourite ride in the world. We went on this ride twice, once near the back and once up the front. During this I was tripping pretty hard, I could see swirling patterns on the floor and the ride before I got on it, M had a few visuals if he concentrated hard enough, I thought heíd passed out when we got on for the first time because he was relaxing and trying for closed eye visuals. The ride itself did make me feel as though I was flying. The second time we went on it we had thoughts of ascending to heaven as the ride moved us up towards the sky.
The next ride was Air, another rollercoaster that is designed to make you feel like you are flying. The queue was quite long, we had a good few giggles and the guy in front of us looked liked a woman, we think we may have been a bit obvious with our chuckles, but we didnít care. Just as we got to the front of the queue M seemed to be coming up a bit stronger, which delighted him beyond belief considering he thought he was as high as heíd get. The ride was fantastic, the sky looked so beautiful, I screamed ĎI can see Godí as we twisted through a corkscrew. We landed.
We were both having difficulty walking now, much like a diphenhydramine trip, we felt quite heavy, the dizziness from rides was affecting us for longer than C and P. We both felt like we wanted a line of coke and M mentioned it would be good if we had some MDMA on us. We decided that it was time for the ghost train, a crazy idea, but we were both up for it. The ghost train there is like a game, the four of us got into a train and you get a gun each to shoot at targets. Itís not a particularly scary ride, but M jumped a few times. On leaving he declared that ĎSome bits had scared meí, but I thought he said Ďsome bitched had scared meí I could not see the bitch, but I believed what he had said. By now we were both on a peak of the high, talking complete trippy bollocks to one another and keeping C and P entertained.
We decided to take a break and we sat down. M bought some chips and found he was staring into the worker's soul as she tried to get him to pay the £1.30; he just gave her a handful of change. She could sort through it all. As he came back to the table he looked incredibly pleased with himself for buying the chips. He didnít finish them. He got a major case of the giggles and some guy with an attitude thought he was laughing at him, luckily we diffused his complaints. Otherwise a fight would have occurred.
By now I should say that Iíve been pissing like a hose pipe all day, and once again I needed the toilet so we walked to find one. I had to ask at a stall as I approached the stall the people working there were all laughing very hard at something, I didnít quite understand what was going on so tried to laugh along with them and look sober, it didnít work too well and I got some very strange looks. They told me where the toilet was but I went in completely the opposite direction back to M, C and P. At this point Mís face had seemed to have collapsed which scared me a little, but I didnít say anything in case he freaked out.
When I got back I found M staring at the floor, he had colourful visuals of the floors crazy paving moving around. We began walking to the next ride and even when we had begun to queue we didnít really know where we were going, walking and queuing were just as fun as the rides. As we got onto the next rollercoaster (the runaway train) M decided the guy announcing safety procedures was a DJ called Fenriz (a Norwegian black metal musician). Fenriz growled to us as the ride began. We had very little idea that we were actually on a ride, we spent the entire time conversing as if we were merely sitting in a pub for the afternoon.
After this the rides we went on seemed part of normality, it didnít really occur to us that we were at a theme park anymore or that we were on rides. The last ride we went on was called Rita, Queen of speed, an extremely fast rollercoaster, however whilst queuing for this ride we had both begun to come down. The kind of slow gentle come down which is fairly enjoyable, although by this point we wanted to go home and chill out. The ride itself was quite good although not as fun as previous rides due to the come down setting in. After this we started to make our way back to the car. On the monorail back to the car park there was a couple sat with us whom I didnít realise were real, then M preceded to talk loudly about what drugs we had taken during the day, in order to play with the couple. The couple seemed quite anxious to get off the monorail and to put as much distance between us and them as possible.
The car ride home was extremely enjoyable, we realised that we werenít coming down as fast as we thought and were still experiencing moderate effects. No-one really spoke for the first hour of the journey as we were both in our own little worlds. I was playing live on stage at a festival and M felt like he was in a film watching the world go by. We then arrived home, said goodbye to C and P and went to our local Pub for a few quiet beers. We both sat feeling extremely good about ourselves and chatted extensively about how ĎPhatí and ĎMintí the day had been. By this point we were both pretty much completely down. All in all an extremely enjoyable day.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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FOI release
A request and disclosure to provide information about pensions funded by the department between 2007 and 2010.
Can the department provide the following information about pensions funded by the department in 2007-08, 2008-09 and 2009-10? Please state
how many staff retired from the organisation in each year?
a) employees
b) consultants
c) workers in any other paid capacity?
Since 1970 the Office of National Statistics (ONS) and Cabinet Office have, on behalf of the Government, produced an annual collection of civil service data known as Mandate (1970-2005, run by Cabinet Office) and the Annual Civil Service Employment Survey (ACSES) (2005-date, run by ONS). Reasons for leaving are one of the elements of data that all government departments are required to submit each year for analysis and publication.
Cabinet Office’s Mandate time series publications are available.
Where some data is not published, this is normally because the level of data quality falls below that which can be relied upon for strategic analysis and statistical purposes.
Please note that the Department for Children Schools and Families became the Department for Education on 12 May 2010.
With reference to question 1: The total number of staff to retire from the organisation (Department for Children Schools and Families (DCSF) 2007-10):
| Name | Term of Office | Political activity declared | Remuneration | Other public appointments |
|---|---|---|---|---|
| Jull Pullen | Three years | None | £300 per day for approximately 25 days per year |
NHS non executive director
schools adjudicator |
The answer is based on those members going from an active member status (i.e. employed by the department) to a pensioner status. It therefore excludes members who have taken payment, whether early (after age 50) or at normal retirement age (age 60), of their preserved benefits. Preserved benefits are where a member has left the employment of the department but whose benefit entitlement has not been put into payment as they are either not entitled to claim it or they have chosen not to claim it at the point of leaving employment.
With reference to question 2: The number of staff who began drawing a pension accrued from their service at the organisation (DCSF):
Financial year 2007-2008 2008-2009 2009- 2010
| Financial year | 2007–2008 | 2008–2009 | 2009– 2010 |
|---|---|---|---|
|
Total number of individuals
drawing a pension |
114 | 83 | 110 |
The answer is based on the answer to Q1 but includes those members who claimed payment of their preserved benefits, whether early (after age 50) or at normal retirement age (age 60).
With reference to question 3: How many staff took early retirement (DCSF):
| Financial year | 2007–2008 | 2008–2009 | <2009–2010 |
|---|---|---|---|
| Number of early retirees | 63 | 23 | 51 |
The answer is based on those members going from an active member status to a pensioner status, who was aged, 50 or over but under age 60. It therefore excludes members who have taken payment of the preserved retirement benefits early, i.e. before age 60. It also excludes those members who have retired on ill health grounds.
With reference to the fourth question 4: The average age of staff retiring from DCSF:
| Financial year | 2007–2008 | 2008–2009 | 2009–2010 |
|---|---|---|---|
| Average age of retirees | 56 | 58 | 55 |
The answer is based on the members identified for Q1 and therefore only includes those going from an active member status to a pensioner status, therefore excluding those members who have taken payment of their preserved benefits.
With reference to question 5: The youngest age of a member of staff currently receiving a pension accrued while working for the organisation (DCSF):
The youngest member is 20 years old and in receipt of ill-health retirement (2009). The earliest that members can receive their pension is normally age 50 (55 if they joined after 01/04/2006), however they would receive lower benefits if they claim before they reach pension age. The answer is the youngest age of any member currently receiving a pension from the PCSPS who worked for the department. This includes those members who have claimed payment of their preserved benefits.
With reference to question 6: The number of staff under 55 who began claiming a pension funded by the organisation (DCSF):
| Financial year | 2007–2008 | 2008–2009 | 2009–2010 |
|---|---|---|---|
| Number of staff under 55 | 30 | 17 | 32 |
This information is from those who retired from the organisation under compulsory or flexible retirement, approved early retirement, age retirement, ill health retirement or partial retirement. The answer is based the members identified for Q2 whose age when they first began claiming a pension was under 55.
With reference to question 7: Of all individuals currently receiving a pension earned from service at the organisation, how many are still working:
| Number of staff still working (partially retired) | 17 |
|---|
The department only holds data for staff in 7a and the numbers are included in the table. We do not hold central data on staff in receipt of civil service pensions who are currently working for the organisation as consultants or any other capacity, as these staff are not permanently employed and are not members of the Civil Service Pension Scheme.