WARGAMES United States 1983 Directed by John Badham
Cast: Matthew Broderick [David], Ally Sheedy [Jennifer], Barry Corbin [Gen. Beringer], John Wood [Stephen Falken]
Rating: 4 out of 5
It's a fact that a good thriller or action movie doesn't need violence to be good or worth watching. All it takes is potential violence to make the audience bite their nails. And what kind of violence could be more efficient than a global thermo-nuclear war?
And potential violence is the premiss WarGames is built upon. A computer whiz-kid, David, is usually contended with hacking into the school computer to change his grades. But after having read an advertisment for an upcoming computer game, he wants to be the first to play it. Instead of getting into the computer at Protovision Software he accidentally comes to the frontgate of NORAD. With the help of Jennifer, a girl in his biology class, he starts to play a nice game of nuclear war with WOPR, War Operation Plan Response (a top-notch computer at the time, but now hardly faster than my own computer)
I remember being 12 and watching this at the cinema. It was very efficent at giving you a scare in those days and that hasn't changed. Of course, a nuclear war seemed to be much more of something that could actually happen back in 1983. I wouldn't be surprised if this gave people nightmares. I hope it still does since you still hear about school kids hacking into the computers at Pentagon.
Broderick and Sheedy are just fine as the young teenagers (although her interest in David remains a mystery). But the rest of the actors are, by no fault of their own, restricted by the script to play one-dimensional grownups. David's parents are the same kind of parents that populate all these college comedies from the 1980s, movies like Secret Admirer, Porky's or Zapped and the NORAD general Beringer is straight out of Dr. Strangelove.
If for no other reason, you should see this movie to enjoy how far the computer technology has gone since those days. David uses the same telephone for calling friends as calling other computers. The modem is some kind of device that he puts the headset on. Far from any 56K modem my guess is that it's some kind of 2400 bps modem, the kind of technology you now find at your local technical museum. The big computer at NORAD has a lot of flashing lights (just like those you see on Star Trek or any old sf-movie) but it's hardly impressing compared with what you can get today.
What are the lessons we can learn from WarGames? 1. Make sure there are no secret backdoors into military computers. 2. Thank you, whoever it was, for giving us a graphical user interface when we use our computers!
Mattias Thuresson<EMAIL_ADDRESS>980419
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One reason for numbering things like figures and equations is to refer the reader to them, as in "See Figure 3 for more details."
One can generate numbered references to anything for which LaTeX maintains a counter by placing a \label command with some mnemonic name in the appropriate environment. The reference is then generated by use of the \ref command with the same mnemonic.
For example
\begin{equation} \label{eqno1} c \geq \sqrt{a^2+b^2+1} \end{equation} .... intervening text ... From Eq.~(\ref{eqno1}) we see ...
Assuming that this is the first equation, the last line will produce "From Eq. (1) we see ..." Note the use of the ~ to ensure that there is no line break between "Eq." and "(1)." Also note that the \ref command produces only the number; if you want the number to be in parentheses (as shown here) you have to include them in your text.
Bibliographic citations work somewhat differently. The following should be consulted:
See also
Return to LaTeX Table of Contents


|
Class Summary |
| ActionEvaluator |
|
| ActionEvaluator.NameValuePair |
|
| ActionLexer |
|
| ActionParser |
Parse the individual attribute expressions |
| AngleBracketTemplateLexer |
Break up an input text stream into chunks of either plain text
or template actions in "<...>". |
| ArrayIterator |
Iterator for an array so I don't have to copy the array to a List
just to make it iteratable. |
| ArrayWrappedInList |
Turn an array into a List; subclass ArrayList for easy development, but
it really doesn't use super stuff for anything. |
| ASTExpr |
A single string template expression enclosed in $...; separator=...$
parsed into an AST chunk to be evaluated. |
| Cat |
Given a list of attributes, return the combined elements in a list. |
| ChunkToken |
Tracks the various string and attribute chunks discovered
by the lexer. |
| ConditionalExpr |
A conditional reference to an embedded subtemplate. |
| ConditionalExpr.ElseIfClauseData |
|
| DefaultTemplateLexer |
Break up an input text stream into chunks of either plain text
or template actions in "$...$". |
| Expr |
A string template expression embedded within the template. |
| FormalArgument |
Represents the name of a formal argument
defined in a template:
group test;
test(a,b) : "$a$ $b$"
t() : "blort"
Each template has a set of these formal arguments or uses
a placeholder object: UNKNOWN (indicating that no arguments
were specified such as when a template is loaded from a file.st). |
| GroupLexer |
|
| GroupParser |
Match a group of template definitions beginning
with a group name declaration. |
| InterfaceLexer |
|
| InterfaceParser |
Match an ST group interface. |
| NewlineRef |
Represents a newline. |
| StringRef |
Represents a chunk of just simple text to spit out; nothing to "evaluate" |
| StringTemplateAST |
|
| StringTemplateToken |
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| TemplateParser |
A parser used to break up a single template into chunks, text literals
and attribute expressions. |


| MATLAB 文件帮助: cv.ConjGradSolver | 查看 cv.ConjGradSolver 的代码 | Index |
ConjGradSolver Non-linear non-constrained minimization of a function with known gradient.
defined on an n
-dimensional Euclidean space, using the Nonlinear
Conjugate Gradient method. The implementation was done based on the
beautifully clear explanatory article:
An Introduction to the Conjugate Gradient Method Without the Agonizing Pain http://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf by Jonathan Richard Shewchuk.
The method can be seen as an adaptation of a standard Conjugate Gradient method (see, for example http://en.wikipedia.org/wiki/Conjugate_gradient_method) for numerically solving the systems of linear equations.
It should be noted, that this method, although deterministic, is rather a heuristic method and therefore may converge to a local minima, not necessary a global one. What is even more disastrous, most of its behaviour is ruled by gradient, therefore it essentially cannot distinguish between local minima and maxima. Therefore, if it starts sufficiently near to the local maximum, it may converge to it. Another obvious restriction is that it should be possible to compute the gradient of a function at any point, thus it is preferable to have analytic expression for gradient and computational burden should be born by the user.
The latter responsibility is accompilished via the getGradient
method of
the function being optimized). This method takes point a point in
n
-dimensional space (input argument represents the array of
coordinates of that point) and comput its gradient (it should be
returned in the output as an array).
NOTE: term criteria should meet one of the following conditions:
type == 'Count+EPS' && epsilon > 0 && maxCount > 0
type == 'Count' && maxCount > 0
| Superclasses | handle |
| Sealed | false |
| Construct on load | false |
| ConjGradSolver | Creates an ConjGradSolver object. |
| ObjectiveFunction | The optimized function represented by a structure with the |
| TermCriteria | Terminal criteria for solver. |
| id | Object ID |
| addlistener | Add listener for event. | |
| clear | Clears the algorithm state. | |
| delete | Destructor | |
| empty | Returns true if the Algorithm is empty. | |
| eq | == (EQ) Test handle equality. | |
| findobj | Find objects matching specified conditions. | |
| findprop | Find property of MATLAB handle object. | |
| ge | >= (GE) Greater than or equal relation for handles. | |
| getDefaultName | Returns the algorithm string identifier. | |
| gt | > (GT) Greater than relation for handles. | |
| Sealed | isvalid | Test handle validity. |
| le | <= (LE) Less than or equal relation for handles. | |
| load | ||
| lt | < (LT) Less than relation for handles. | |
| minimize | Runs the algorithm and performs the minimization. | |
| ne | ~= (NE) Not equal relation for handles. | |
| notify | Notify listeners of event. | |
| save |
| ObjectBeingDestroyed | Notifies listeners that a particular object has been destroyed. |


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|
Assimp
5.0.1, master @ cd42b955
|
Describes a MDL7 mesh group. More...
Public Member Functions | |
| IntGroupInfo_MDL7 () AI_NO_EXCEPT | |
| Default constructor. More... | |
| IntGroupInfo_MDL7 (BE_NCONST MDL::Group_MDL7 *_pcGroup, unsigned int _iIndex) | |
| Construction from an existing group header. More... | |
Public Attributes | |
| unsigned int | iIndex |
| Index of the group. More... | |
| BE_NCONST MDL::Group_MDL7 * | pcGroup |
| Points to the header of the group. More... | |
| MDL::Triangle_MDL7 * | pcGroupTris |
| Points to the beginning of the triangle section. More... | |
| BE_NCONST MDL::TexCoord_MDL7 * | pcGroupUVs |
| Points to the beginning of the uv coordinate section. More... | |
| BE_NCONST MDL::Vertex_MDL7 * | pcGroupVerts |
| Points to the beginning of the vertex section. More... | |
Describes a MDL7 mesh group.
|
inline |
Default constructor.
|
inline |
Construction from an existing group header.
| unsigned int Assimp::MDL::IntGroupInfo_MDL7::iIndex |
Index of the group.
| BE_NCONST MDL::Group_MDL7* Assimp::MDL::IntGroupInfo_MDL7::pcGroup |
Points to the header of the group.
| MDL::Triangle_MDL7* Assimp::MDL::IntGroupInfo_MDL7::pcGroupTris |
Points to the beginning of the triangle section.
| BE_NCONST MDL::TexCoord_MDL7* Assimp::MDL::IntGroupInfo_MDL7::pcGroupUVs |
Points to the beginning of the uv coordinate section.
| BE_NCONST MDL::Vertex_MDL7* Assimp::MDL::IntGroupInfo_MDL7::pcGroupVerts |
Points to the beginning of the vertex section.


notes on
everlasting options
A few weeks ago, I read
@_Dave__White_ and
@SBF_FTX's
paper on
everlasting options for the first time.
A few things surprised me and I became interested in trying to ‘warp’ them
with different compositional assumptions. I jotted down some notes and
tweeted some takeaways here [LINK TO TWEETS ONCE YOU TWEET].
QUESTIONS
I was curious to better understand some of the dynamics of EOs.
Specifically:
1 – How does the mark, funding fee, and funding fee volatility of an EO
change as a function of FF (funding frequency)?
2 – What are the trade-offs of choosing a lower vs. higher FF?
3 – What happens if we try to manipulate the composition of an EO's
equivalent portfolio by replacing the FF-defined weighting term with other
geometric series?
1.a – RELATIONSHIP BETWEEN FF AND THE MARK OF AN EO
It’s possible to answer these questions just by manipulating the
Black-Scholes formula\(^1\) but I thought it would be more fun (and more
digestible) to try to observe these relationships tangibly. So I pulled some
data on Apple call options and constructed an artificial EO call on Apple at
a $140 strike.
My colab workbook is
here.
And the underlying data with my janky excel clean up work is
here.
This
is the csv export I reference in the colab workbook. (Data is sourced from
the
Chicago Board Options Exchange.)
(Note: I used a $140 strike because it’s the most commonly traded call
strike for Apple at the moment.)
There’s a small issue with trying to compute an EO using real-world option
prices – we actually won’t have options to reference for every major
expiration term in our EO.
How might we then establish an 'equivalent portfolio' to reference for
pricing our EO? I used the options that were available to derive a best-fit
curve so I could then infer prices for daily maturities. This allowed me to
construct an 'equivalent portfolio' with realistic regprices for calls that
don’t actually exist on CBOE. Real-world options will vary slightly from
this curve, which creates arbitrage opportunities; at scale, if EOs are
broadly traded, I imagine they should impact the price of traditional
options, forcing them to trade along an EO curve like what’s computed below:
We can see that Apple calls with a $140 strike can be roughly priced
according to the line:
y = 0.57829 * x^0.55844 + 2.15977
Where y = price of a traditional expiring option that’s x days from
expiration.
(It’s helpful to note this is a root function, which means it doesn’t have a
horizontal asymptote. If it did, then we may not need EOs at all. We could
instead buy an option with an expiration infinitely far out into the future
and pay a high finite value for it.)
So what’s the impact of this price curve on the mark of our EO?
As FF increases, the composition of options in the equivalent portfolio
flattens, shifting more of the weight of the EO into the tail.\(^2\)
Since later expirations are more expensive, we should expect the mark of an
EO to increase as a function of FF.
Calculating the mark of different FFs shows us that our mark indeed
increases, also following a root function.\(^3\)
1.b – RELATIONSHIP BETWEEN FF AND THE FUNDING FEE OF AN EO
Recall, funding fee = (1/FF)(mark - payoff).
As FF increases, we should see the funding fee decrease due to the term
1/FF. However, as noted above, we should also expect our mark to increase as
FF increases.
What’s the net of these two impacts? Since our option price only scales as a
root function of maturity (scaling more slowly than 1/FF decays), our fee
ends up dropping as FF grows.
It’s interesting to consider that as FF increases, the mark paid upfront for an EO should increase, but the incremental funding fee required to roll it fwd should decrease.
1.c – RELATIONSHIP BETWEEN FF AND THE FUNDING FEE VOLATILITY OF AN EO
How much should we expect our funding fee to vary from period to period as a
result of choosing one FF over another?
This is a bit trickier. If there’s no change in the underlying spot, then we
shouldn’t see any change to our funding fee, so this is really a
3-dimensional sensitivity analysis of the funding fee relative to the
underlying spot and potential FFs.
To assess price fluctuations across varying spot prices, I needed to model
price as a function of spot values. Our real-world dataset sadly doesn’t
provide historical option prices according to varying spots, so I couldn’t
derive the relationship with real data. Instead I relied on the
Merton-adjusted Black-Scholes call formula.
Tweaking the inputs a bit, I was able to get the Black-Scholes curve sorta
close to our best-fit curve. Black-Scholes slightly under-prices in the
near-term and over-prices in the outlying maturities relative to the
real-world benchmark. There are likely a bunch of flaws in the way I’ve
implemented Black-Scholes (e.g., using a static risk-free interest rate
across all maturities lol), but for my purposes this is close enough and
gives me a way to observe price movements as a function of the spot.
Looking at this Apple call, if the spot shifts 5% above or below our spot
benchmark today ($142.90), we can see that the price changes most for
options with near-term expirations and relatively less so for options with
later expirations.
Conceptually this makes sense: If we think of options as comprising both
‘intrinsic value’ (driven by the delta between their strike and spot) and
‘time value’ (driven by time to expiration), time value will make up a
greater % of later expirations’ price and so changes in the intrinsic value
will have a relatively muted effect on overall price.
If our EO has a higher FF, we should be more exposed to ‘later expirations.'
As such, the overall mark of our EO is less volatile as spot moves.
What's the net impact on the funding fee? It's easy to calculate funding fee
as a function of spot, so let’s just do it and see:
Great – so the funding fee is more sensitive at lower FFs.
But something else surprised me in seeing this – the funding fee actually
goes down as the call moves further into or out of the money. This
was super weird to me at first and I assumed I’d made a mistake somewhere.
To better understand this, I decided to look at the funding fee of two EOs –
one set to FF=1 and another with FF=5 – across a range of spots:
We can see our funding fee is highest right at the threshold of being in the
money (recall: strike = $140), where our EO mark is maximized but we are
still expecting a payoff of ~0. It’s interesting to see that as FF increases
to 5, the peak fee drops dramatically, in this case by ~80%.
The cumulative impact of these dynamics is interesting to consider: For an
EO that’s close to the money, a lower FF will increase both the funding fee
and the relative variance of the funding fee period-to-period.
This might create risk for liquidity-challenged longs holding
deep-in-the-money EO calls. If there's an intermittent drop in the spot,
they may be forced to lapse on escalating funding fees, causing their
portfolios to expire. This is a boon for shorts, whose payoff obligations
decrease in this scenario just as they are on the hook to fund payoffs. As
can be seen, at lower FFs, a few % shift in the underlying spot can create
orders of magnitude increases in fees for longs. Super interesting!
Overall, I was really surprised by the cashflow dynamics of EOs – it would
appear there are a number of ways they could be manipulated by both longs
and shorts to maximize returns.\(^4\)
2 – WHAT ARE THE TRADE-OFFS OF CHOOSING A LOWER VS. HIGHER FF?
Pausing here, I tried to think about practical applications for EOs.
One idea that came to mind: Using an EO to hedge against intermittent
movements in a soft-pegged stablecoin. I was curious how the liquidation
crunch that took place in Dai could have been contained if those who held
Dai as collateral could have purchased a Dai-based EO ‘put.’ The funding fee
effectively becomes an insurance premium paid by longs to shorts to receive
a $ payoff if/when Dai experiences an intermittent drop relative to $.
Letting an EO put expire would allow longs to receive payoffs as the
underlying sinks, helping to cover escalating near-term collateral
obligations and thereby helping to abate cascading liquidations.
Here, the timeline by which a price fluctuation is expected to cascade
through the market can be a helpful determinant for the relevant funding
frequency / funding period assumptions for the EO. I would ideally want to
use a funding frequency that minimizes my fees and fee volatility, but still
gives me sufficient near-term payoffs in the case of a downward price
movement in the underlying. It would be fun to determine the ideal terms
using historical data from past liquidation crunches.
3 – 'WARPING' EOs
[EMBED TWEETS ON EVERLASTING OPTIONS HERE]
Overall, what’s the point of these alternative weightings?
As we’ve already observed, altering the composition of an EO has an impact
on the mark, funding fee, and funding fee volatility rate of an EO.
But I actually think the most interesting consequence of a 'warped' EO is
that, in the case where a funding fee is not paid, the long position will
close at a varying pace, meaning that shorts might have more notice of a
major upcoming funding obligation to longs who are letting their EOs expire.
This has important implications for shorts and could also have consequences
for the type and amount of collateral that shorts might have to post.
There's a lot to unpack in the above, but before getting ahead of myself,
it’s important to mention the underlying flaw with ‘warping’ EOs.
In brief, the funding fee of these EOs becomes disjointed from their mark
and payoffs. The funding fee becomes equivalent to opening a new position in
a different portfolio of options at each funding period, required a highly
fractured pool of short/long positions.
Let’s walk through this in simple steps.
Recall that an EO is designed to expire in regular intervals. In order to
roll a position forward, a trader who is long has to, effectively, buy more
options such that they ‘replace’ what’s expiring at each interval.
What happens in the case of a ‘warped’ EO?
Consider a portfolio that’s weighted by the convergent series n/2^(n+1). The
weight terms of the equivalent portfolio become:
If we were to try to buy more of the same portfolio, with the intention of
preserving the composition of the portfolio post-expiration, we would want
to solve for x in the following function (where x is a multiple of our
portfolio that we have to buy pre-expiration):
$$x\left({n\over{2^{n+1}}}\right) = {n\over{2^n}}$$
$$x = {2n-2\over{n}}$$
Note: Here, the term n can't be factored out, which effectively means we
have to buy a different amount of each term in order to succcessfully roll
our portfolio fwd.
The amount we’d actually have to buy of each term would be:
$$\left({2n-2\over{n}}\right)\left(n\over{2^{n+1}}\right) -
\left({n}\over{2^{n+1}}\right)$$
Which reduces to: $${n^2-2n}\over{n2^{n+1}}$$
Phew! Try it out. If we want to make our third term equivalent to our second
by buying more of the third term, our n=3 and we’d have to buy \(2^{-4}\).
Does that work? Our third term in our EO has a weight of 3/16. If we buy
1/16 of it, we end up with 3/16 + 1/16 = 4/16 = 1/4 = the right weight for
our second term.
We can actually visualize how much of each option we’d have to buy for our
portfolio to roll fwd successfully – shown below in green:
As you can see, this ‘green’ portfolio’s composition is different from our
EO’s composition. Interestingly, this purchase would be akin to buying a
quarter of our existing EO, but with our date index starting 2 funding
periods later. If we multiply the terms by 4 and shift the x axis to the
left by 2, you can see our curves overlapping with each other
here (click on
the purple curve to visualize it over the red).
In effect: $${4{(n+2)^2-2(n+2)}\over{(n+2)2^{n+2}}} = {n\over{2^{n+1}}}$$
Which is kind of wild!
But this is also akin to buying another EO altogether. Each funding period,
we'd need a different portfolio, indexed to another 2 periods later, to
successfully roll fwd.
This creates some tough issues, notably that there needs to be a new short
portfolio offered every funding period for a given EO to successfully ‘gross
up’ positions. There are also added complexities if a long intermittently
skips funding periods, creating ‘warped’ exposures for both longs and
shorts. With a constant-rate decaying EO, we can pool all short positions
for a given strike together and the ‘gross up’ portfolio required to roll
fwd turns out to be the EO itself, ultimately increasing market liquidity
around a single instrument.
This issue is even more complicated for series that don’t monotonically
decay from n=1 to infinity, such as our earlier example of ln(n)/(5(n-5)^2
+5). The math is really ugly but it’s easy to visualize the mechanics of
what would have to happen – in this case, because of the hump around the 5th
term, as longs who want to roll our portfolios fwd we’d actually have to go
short on a chunk of the fifth term so we’re not overweight post-expiration
when those options become our fourth term. In essence, we have to open a
bunch of new long and one notable short position to roll fwd – and our
funding fee is effectively the delta between these increasingly complicated
cashflows.
To simplify, if we were to roll a ‘warped’ EO fwd, our funding fee could be
thought of as (mark2 - (first weight term)*payoff), where mark2 indicates
the purchase of the “gross up” portfolio for that EO as defined above.
Oof!
But the truth is, since we’re dealing with a derivative of an imaginary
options portfolio, these ‘warped’ EOs are perfectly valid. They’re just
challenging to track if you’re trying to do the math on pen and paper and
they greatly fracture the pooling of short/long positions. That's pretty
bad, but maybe the most damning criticism of these EOs is that they may have
no real utility. I’d be very curious if there’s ever a need to hedge by a
specific count of days into the future vs. holding an EO that is most
exposed to its next immediate expiration and less so going forward. If there
is utility, I'd love to hear about relevant cases. If there's such a
scenario, it may still make sense for specialized shorts to offer these as
bespoke instruments.
Waow, you’ve really made it all the way to the end! Send me an email at
<EMAIL_ADDRESS>– I want to hear from you.
Outstanding questions:
In the case where a long lapses on funding payments, how long will shorts
continue distributing payoffs before their obligations are considered
closed?
Does the price of an EO need to have a discount term applied to it to
account for a ‘terminal payment?’
What’s the right framework for thinking about posted collateral? How is this
handled in traditional options today? What’s the effective net P&L for
shorts / longs?
Footnotes:
\(1–\)
We could answer this by taking the partial derivative of the Black-Scholes
formula corresponding to ∂C/∂t, with C and t defined below. But I find it
hard to build intuition around the dynamics of the terms at this level given
the complexity of the Black-Scholes formula.
\(2–\)
In the pricing formula provided in appendix B of the paper, the price of
each constituent option in the 'equivalent portfolio' is multiplied by a
term defined by the EO’s ‘funding frequency’ (referred to as FF from here
on):
$$\left({1\over{FF}}\right)\left({FF\over{FF+1}}\right)^n$$
Where n refers to the index of the option in the equivalent basket. Note:
the term n follows a 1-based index, so the first option to expire in our
equivalent portfolio has an index of 1, not 0.
In the case where FF = 1, we get the very clean series \(1\over2^n\). As
shown in the paper, this results in the following weightings (shown as a
continuous line):
What happens if funding is paid twice per day? FF = 2 and our series now turns into \(2^{(n-1)}\over{3^n}\). This series starts with the term 1/3 and decays by 2/3 for each term:
There’s an interesting relationship between the FF and our portfolio weighting – as our frequency increases, our portfolio becomes more evenly weighted. In the limit, we effectively hold an equal and infinitesimally small amount of each option in the equivalent portfolio.
\(3–\)
I later learned this curve is sometimes used to describe the ‘time-value’ of
an option – or alternatively the ‘theta decay’ as an option approaches its
expiration date. In effect, the change in an option’s value from t = 1 to t
= 2 is going to be relatively greater than the change from t = 300 to t =
301 (as observed in the Apple call price curve). This intuitively makes
sense: In the first instance we’re doubling the amount of time we have for
our option to realize a payoff, and in the latter we’re adding a measly
33bps of time to our maturity window.
\(4–\)
EOs are in principle very similar to perps, but since the payoff of an
option is always ≥$0, we end up with a bunch of interesting asymmetries for
which there are no corollaries in perps. Three noteworthy considerations:
- All else being equal, a later-expiring option will always be more
expensive than a nearer expiration
- As such, the mark of an EO (which is effectively a weighted average price
of all future expirations) will always be ≥ the payoff of an equivalent
traditional option expiring at the EO’s next funding period
- Therefore, following the funding fee formula of (1/FF)(mark - payoff), we
can see that the funding fee of an EO never turns negative
This means that longs always have to pay the funding fee to shorts. However,
longs aren’t obligated to pay the funding fee! They could lapse, in which
case shorts are obligated to fund payoffs to longs, shifting the direction
of cash flows as the EO partially expires.
What’s interesting about this relationship is that shorts don’t know until
the instant of expiration whether they are, in net, receiving or paying
funds, and longs have to believe in the long-term liquidity of shorts to be
able to fund incremental payoffs in perpetuity (recognizing that at some
point, the value of each payoff will be infinitesimal).
If I were long an EO with a high FF, I might expect my counterpart short to
have to post some collateral proving liquidity N periods out into the
future. If a short defaults on a payoff, or if the value of the underlying
shifts dramatically (skyrockets for a call / plummets for a put) such that
the posted collateral falls below the expected N-period threshold, we might
expect shorts to face margin calls. If a long also suspects a short to be
illiquid across the short’s portfolio, the long should be expected to stop
funding EO payments and force collateral liquidation.
Scratchpad:
How does the funding fee mechanism work and how can we derive this funding
fee term ourselves?
To understand what’s actually happening here, it’s helpful to walk through
the funding mechanics tangibly as laid out in the paper.
Take the vanilla EO with a FF = 1 (the 1/2^n series).
The sum of this infinite series is 1, which means that in buying this EO, we
effectively own 1 option broken up into 0.5 of an option expiring today,
0.25 of an option expiring tomorrow, and so on, as mentioned in the paper.
If we were to buy this EO and just hold it, after the first period, half of
our ‘equivalent portfolio’ will have expired.
For an option to be *~everlasting~*, after each funding period we would
ideally expect to own the same nominal volume of options and maintain the
same portfolio composition, we’d just want it dated one period into the
future.
The paper does a great job of walking through this mechanic so I’ll try to
avoid being redundant, but in effect, if we’re long an EO, we can purchase
more of the same EO just a moment before an expiration period and thereby
ensure that our nominal position volume and portfolio composition don’t
change. Because the expirations result in a payoff, we actually end up
having to pay just the difference between the mark of the EO and the value
of its next immediate payoff.
When FF=1, we just double our position in the EO right before an expiration
and we end up realizing the payoff equivalent to owning a conventional
option set to expire at that moment.
Note: I didn't have to close any positions to roll fwd. In effect, the EO
partially closes on its own.
What happens to this funding fee if our funding frequency increases?
To answer this question, it’s first helpful to understand how much we have
to gross up each portfolio in order to successfully roll through expiration
periods.
Grossing up the portfolio effectively means multiplying all of our positions
by an amount X such that our weights for positions (n+1) equal weights for
position n. For example, for our vanilla EO of 1/2^n, we are trying to solve
for:
$$x\left({1\over{2^{n}}}\right) = {1\over{2^{n-1}}}$$
$$x = 2$$
Generalizing for all EOs:
$$x\left({1\over{FF}}\right)\left({FF\over{FF+1}}\right)^{n} =
\left({1\over{FF}}\right)\left({FF\over{FF+1}}\right)^{n-1}$$
$$x = {FF+1\over{FF}}$$
Technically since we presumably already own a finite 1 EO already, to gross
up, we can buy x-1 EOs, which simplifies to \(\left({FF+1}\over{FF}\right) -
1 = {1\over{FF}}\).
Let’s try this out on an EO with FF = 3. This is what the portfolio looks
like before our first expiration:
According to our term 1/FF, we buy 1/3 more of the same EO a moment before the expiration, creating the following portfolio:
After our first expiration, our portfolio has rolled fwd and we end up
earning the equivalent of 1/3 the payoff of the equivalent option expiring
today.
Generalized, our funding fee = (1/FF)*(mark - payoff).


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The Workspace stand at the Eurobot Exhibition in Frankfurt, Germany
[
Return to Home Page
]
Workspace
and
Robotrak
are registered trademarks of Robot Simulations Ltd.
Copyright © 1996 Robot Simulations Ltd
Last revision 3 April 1996


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view in staging
Chart Builder
Mobile (300px)
Desktop (700px)
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{% if COPY_GOOGLE_DOC_KEY %} {% if COPY_GOOGLE_DOC_KEY != 'None' %}
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In a normal testing scenario, a failed test might mean that you would have to tie up the QA environment until a developer can login to diagnose, triage, and remediate. With the Delphix DDP, data is never actually deleted or destroyed, except by policy. This means we can free up our environments for further testing, development, etc., as we can recall the previous data state into our data pods at any given time.
In this scene, we are going to refresh the data in our Test
data pod and manually redeploy the Application to a stable state,
just to demonstrate the features stated above…
YOUR STEPS FOR THIS SCENE:
- Activate the Delphix Engine tab
- Login as qa, if your login has timed out.
- Click on
Test
- Click the REFRESH icon (purple rectangle)
- Click
REFRESH
in the dialog box to confirm (purple rectangle) - Monitor your refresh progress
- Once the refresh is complete, we need to redeploy the QA app from a stable state. This takes about 60 seconds.
- Open a terminal window and type
deploy_qa HEAD~3
- Once that is complete, click the
Patients QA
bookmark to refresh the page - You should now see
Patients QA
populated with the masked data from production, in a “test ready” state. If you click on theUsers
button, you should see we are back to the correct users.


AlloyUI - Char counter
character(s) remaining
set maxLength to 10
set maxLength to 20
character(s) remaining
character(s) remaining


--- title: Gaussian Expectation Propagation author: Mauro Camara Escudero date: '2020-03-12' slug: gaussian-expectation-propagation categories: - expectation-propagation tags: - gaussian-ep - gaussian-expectation-propagation - normal-expectation-propagation subtitle: '' summary: 'Description of Expectation Propagation using Multivariate Normal factors for the global approximation.' authors: [] lastmod: '2020-03-12T19:47:53Z' featured: no image: caption: '' focal_point: '' preview_only: no projects: [] bibliography: bibliography.bib ---
Remember from a previous blog post that a pdf \(p(\boldsymbol{\mathbf{x}}) = \mathcal{N}(\boldsymbol{\mathbf{x}}; \boldsymbol{\mathbf{\mu}}, \boldsymbol{\mathbf{\Sigma}})\) can be written as \[ p(\boldsymbol{\mathbf{x}}) = \exp\left\{\langle \boldsymbol{\mathbf{\Sigma}}^{-1}\boldsymbol{\mathbf{\mu}}, \boldsymbol{\mathbf{x}}\rangle + \left\langle \text{vec}\left(-\frac{1}{2}\boldsymbol{\mathbf{\Sigma}}^{-1}\right), \text{vec}\left(\boldsymbol{\mathbf{x}}\boldsymbol{\mathbf{x}}^\top\right)\right\rangle -\frac{1}{2}\left[d\log2\pi + \log|\boldsymbol{\mathbf{\Sigma}}| +\boldsymbol{\mathbf{\mu}}^\top\boldsymbol{\mathbf{\Sigma}}^{-1}\boldsymbol{\mathbf{\mu}}\right]\right\} \]
Following the work of Barthelmé, Chopin, and Cottet (2015) we can relabel the expression above as follows \[ p(\boldsymbol{\mathbf{x}}) \propto \exp\left\{\langle \boldsymbol{\mathbf{r}}, \boldsymbol{\mathbf{x}}\rangle + \left\langle \text{vec}\left(-\frac{1}{2}\boldsymbol{\mathbf{Q}}\right), \text{vec}\left(\boldsymbol{\mathbf{x}}\boldsymbol{\mathbf{x}}^\top\right)\right\rangle \right\} \] where we have defined \[ \boldsymbol{\mathbf{r}}:= \boldsymbol{\mathbf{\Sigma}}^{-1} \boldsymbol{\mathbf{\mu}}\qquad \text{and} \qquad \boldsymbol{\mathbf{Q}}:= \boldsymbol{\mathbf{\Sigma}}^{-1} \]
We assume the target distribution is intractable, but can be factorized into a product of \(K+1\) terms \[ f(\boldsymbol{\mathbf{\gamma}}) \propto \prod_{k=0}^K f_k(\boldsymbol{\mathbf{\gamma}}_k) \] where usually
From here onwards we assume that the prior distribution \(f_0(\boldsymbol{\mathbf{\gamma}})\) is a multivariate gaussian distribution \(\mathcal{N}(\boldsymbol{\mathbf{\gamma}}; \boldsymbol{\mathbf{r}}_0, \boldsymbol{\mathbf{Q}}_0)\) (with natural parameters \(\boldsymbol{\mathbf{\theta}}_0 = \left(\boldsymbol{\mathbf{r}}_0, -\frac{1}{2}\boldsymbol{\mathbf{Q}}_0\right)^\top\)). \[ f_0(\boldsymbol{\mathbf{\gamma}}) = p(\boldsymbol{\mathbf{\gamma}}; \boldsymbol{\mathbf{r}}_0, \boldsymbol{\mathbf{Q}}_0) = \propto \exp\left\{\langle \boldsymbol{\mathbf{r}}_0, \boldsymbol{\mathbf{x}}\rangle + \left\langle \text{vec}\left(-\frac{1}{2}\boldsymbol{\mathbf{Q}}_0\right), \text{vec}\left(\boldsymbol{\mathbf{x}}\boldsymbol{\mathbf{x}}^\top\right)\right\rangle \right\} \]
Note: The parameters \(\boldsymbol{\mathbf{\gamma}}_0, \ldots, \boldsymbol{\mathbf{\gamma}}_k\) are not the components of \(\boldsymbol{\mathbf{\gamma}}\), they are different parameters.
The global approximation is defined as \[ g(\boldsymbol{\mathbf{\theta}}) \propto \prod_{k=0}^K g_k(\boldsymbol{\mathbf{\theta}}_k) \] where we set the first term to be equal to the (tractable) prior, which is a multivariate gaussian distribution \[ g_0(\boldsymbol{\mathbf{\theta}}) \propto \exp\left\{\langle \boldsymbol{\mathbf{r}}_0, \boldsymbol{\mathbf{x}}\rangle + \left\langle \text{vec}\left(-\frac{1}{2}\boldsymbol{\mathbf{Q}}_0\right), \text{vec}\left(\boldsymbol{\mathbf{x}}\boldsymbol{\mathbf{x}}^\top\right)\right\rangle \right\} \] Furthermore, we assume that each factor \(g_k(\boldsymbol{\mathbf{\theta}}_k)\) for \(k=1, \ldots, K\) also follows a Multivariate Normal Distribution with natural parameter \(\boldsymbol{\mathbf{\theta}}_k = (\boldsymbol{\mathbf{r}}_k, \boldsymbol{\mathbf{Q}}_k)^\top\). Luckily, the product of Gaussians is again a Gaussian distribution. In particular we have \[ \begin{align} g(\boldsymbol{\mathbf{\theta}}) &\propto \prod_{k=0}^K \exp\left\{ \boldsymbol{\mathbf{r}}_k^\top\boldsymbol{\mathbf{x}}+ \text{vec}\left(-\frac{1}{2}\boldsymbol{\mathbf{Q}}_k\right)^\top \text{vec}\left(\boldsymbol{\mathbf{x}}\boldsymbol{\mathbf{x}}^\top\right) \right\} \\ &= \exp\left\{\left(\sum_{k=0}^K \boldsymbol{\mathbf{r}}_k\right)^\top\boldsymbol{\mathbf{x}}+ \text{vec}\left(-\frac{1}{2}\sum_{k=0}^K \boldsymbol{\mathbf{Q}}_k\right)^\top \text{vec}\left(\boldsymbol{\mathbf{x}}\boldsymbol{\mathbf{x}}^\top\right)\right\} \\ &:= \exp\left\{\boldsymbol{\mathbf{r}}^\top\boldsymbol{\mathbf{x}}+ \text{vec}\left(-\frac{1}{2}\boldsymbol{\mathbf{Q}}\right)^\top \text{vec}\left(\boldsymbol{\mathbf{x}}\boldsymbol{\mathbf{x}}^\top\right)\right\} \end{align} \] where we have defined the global natural parameters to be \[ \boldsymbol{\mathbf{r}}:= \sum_{k=0}^K \boldsymbol{\mathbf{r}}_k \qquad \text{and} \qquad \boldsymbol{\mathbf{Q}}:= \sum_{k=0}^K \boldsymbol{\mathbf{Q}}_k \] In other words, the natural parameters of the global approximation are found by summing the natural parameters of all the sites.
Note: The parameters \(\boldsymbol{\mathbf{\theta}}_0, \ldots, \boldsymbol{\mathbf{\theta}}_k\) are not the components of \(\boldsymbol{\mathbf{\theta}}\), they are different parameters.
The cavity distribution at the \(k^{\text{th}}\) site is given by the product of all but the \(k^{\text{th}}\) Multivariate Gaussian. This means that we can write the cavity distribution as \[ g_{-k}(\boldsymbol{\mathbf{\theta}}- \boldsymbol{\mathbf{\theta}}_k) \propto \exp\left\{\left(\boldsymbol{\mathbf{r}}- \boldsymbol{\mathbf{r}}_k\right)^\top\boldsymbol{\mathbf{x}}+ \text{vec}\left(-\frac{1}{2}\left(\boldsymbol{\mathbf{Q}}- \boldsymbol{\mathbf{Q}}_k\right)\right)^\top \text{vec}\left(\boldsymbol{\mathbf{x}}\boldsymbol{\mathbf{x}}^\top\right)\right\} \] In other words, the natural parameters of the cavity distribution are found by taking the difference between the global natural parameters and the natural parameters of the \(k^{\text{th}}\) site.
The tilted distribution, also called pseudo-posterior, is found by multiplying the cavity distribution by the \(k^{\text{th}}\) local likelihood term. In other words, the tilted distribution is a (pseudo-)posterior where we use the cavity distribution as a (pseudo-)prior and the single local likelihood term as the likelihood.
In general, computing moments of this distribution will be intractable, however we how that calculating moments of this distribution will be easier than calculating moments of the entire target distribution. \[ \begin{align} g_{\backslash k}(\widetilde{\boldsymbol{\mathbf{\gamma}}}_k) &\propto f_k(\boldsymbol{\mathbf{\gamma}}_k) \exp\left\{\left(\boldsymbol{\mathbf{r}}- \boldsymbol{\mathbf{r}}_k\right)^\top\boldsymbol{\mathbf{x}}+ \text{vec}\left(-\frac{1}{2}\left(\boldsymbol{\mathbf{Q}}- \boldsymbol{\mathbf{Q}}_k\right)\right)^\top \text{vec}\left(\boldsymbol{\mathbf{x}}\boldsymbol{\mathbf{x}}^\top\right)\right\} \end{align} \]
Barthelmé, Simon, Nicolas Chopin, and Vincent Cottet. 2015. “Divide and Conquer in Abc: Expectation-Progagation Algorithms for Likelihood-Free Inference.”


| {{curr_header}} | |||||
|---|---|---|---|---|---|
| 2 | Isidra | Boudreaux | Traffic Court Referee | 22 Jun 1972 | Active |
| 3 | Shona | Woldt | Airline Transport Pilot | 3 Oct 1981 | Disabled |
| 1 | Granville | Leonardo | Business Services Sales Representative | 19 Apr 1969 | Suspended |
| 8 | Easer | Dragoo | Drywall Stripper | 13 Dec 1977 | Active |
| 4 | Maple | Halladay | Aviation Tactical Readiness Officer | 30 Dec 1991 | Suspended |
| 5 | Maxine | Woldt | Business Services Sales Representative | 17 Oct 1987 | Disabled |
| 6 | Lorraine | Mcgaughy | Hemodialysis Technician | 11 Nov 1983 | Disabled |
| 9 | Lizzee | Goodlow | Technical Services Librarian | 1 Nov 1961 | Suspended |
| 10 | Judi | Badgett | Electrical Lineworker | 23 Jun 1981 | Active |
| 7 | Lauri | Hyland | Blackjack Supervisor | 15 Nov 1985 | Suspended |
|
|


We are into Mobile and Desktop App development, Website development, Animations and Photo Designs. The company targets any customer that wishes to take their business to the next level by providing you with the application that makes your customers feel special. We develop and nurture your idea into reality. If you have just a vague idea you want to bring to the software pool, just contact us to help you bring the best out of it.
Shape the future of the technology by creating softwares to make life easier and better.
To the become the world's leading innovational company in the aspects of advancing technological products and services.
A talented and skilled graduate of KNUST, Ghana with St. Augustine's College as an alma mater. He's skilled in Java,Kotlin,JavaScript,CSS,Html,C,C#,Php etc. He's the brain behind the Tanamo Inc.
This geek makes sure our software looks simple and usuable . He has knowledge in Java, HTML, CSS,JavaScript. Currently serving with Tanamo Inc.
A talented and skilled graduate of KNUST, Ghana with St. Augustine's College as an alma mater. He has knowledge in HTML, CSS and JavaScript. Currently serving with Tanamo Inc.


--- name: notification url: /notification --- ## Notification
This notification is static, it works similarly to a programmatic with some subtle differences.
This notification is static, it works similarly to a programmatic with some subtle differences.


Uses of Class
boardgame.Position
| Package | Description |
|---|---|
| boardgame |
This package corresponds to the game board layer.
|
| chess |
This package corresponds to the chess layer.
|
| chess.pieces |
the package chess.pieces will contain the classes that will represent each
type of chess piece.
|
-
Uses of Position in boardgame
Fields in boardgame declared as Position Modifier and Type Field Description protected Position
Piece. position
Methods in boardgame with parameters of type Position Modifier and Type Method Description Piece
Board. piece(Position position)
This method returns a piece belonging to the piece matrix associated with the board given a board position.void
Board. placePiece(Piece piece, Position position)
Put a piece in a position of the pieces matrix belonging to the board.boolean
Board. positionExists(Position position)
check if a certain position exists on the board or not given a positionboolean
Piece. possibleMove(Position position)
determines whether it is possible for a piece to move to a specific position.Piece
Board. removePiece(Position position)
Removes a piece from the board located in the position on the board that was passed as the method parameter.boolean
Board. thereIsAPiece(Position position)
checks whether in a position on the board, there is or is not a piece. -
Uses of Position in chess
Methods in chess that return Position Modifier and Type Method Description protected Position
ChessPosition. toPosition()
Converts a ChessPosition to a Position.Methods in chess with parameters of type Position Modifier and Type Method Description protected static ChessPosition
ChessPosition. fromPosition(Position position)
Converts a Position to ChessPosition.boolean
ChessPiece. isThereOpponentPiece(Position position)
This method is intended to check if there is an opposing piece in a desired position.private Piece
ChessMatch. makeMove(Position source, Position target)
contains the piece move logic of theChessMatch.performChessMove(ChessPosition, ChessPosition)
method.private void
ChessMatch. undoMove(Position source, Position target, Piece capturedPiece)
undoes a chess move. removes the piece that was placed at the move target, and returns it to the move source position.private void
ChessMatch. validateSourcePosition(Position position)
Validates a source position of the movement after verification of compliance of 3 possible conditions.private void
ChessMatch. validateTargetPosition(Position sourcePosition, Position targetPosition)
It will check if the target position is valid, in relation to the source position of a movement. -
Uses of Position in chess.pieces
Methods in chess.pieces with parameters of type Position Modifier and Type Method Description private boolean
King. canMove(Position position)
determines whether the king can move to the position referred to as the method parameter.private boolean
Knight. canMove(Position position)
determines whether a knight can move to the position referred to as the method parameter.private boolean
King. testRookCastling(Position position)
this method tests whether in a given position there is a rook suitable for the castling special move. initially theBoard.piece(Position)
method returns a possible chess piece located in the position that was passed as a parameter.


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java.lang.Object edu.cmu.cs.stage3.image.codec.PNGDecodeParam
public class PNGDecodeParam
An instance of ImageDecodeParam
for decoding images in
the PNG format.
PNGDecodeParam
allows several aspects of the decoding
process for PNG images to be controlled. By default, decoding produces
output images with the following properties:
Images with a bit depth of 8 or less use a
DataBufferByte
to hold the pixel data. 16-bit images
use a DataBufferUShort
.
Palette color images and non-transparent grayscale images with
bit depths of 1, 2, or 4 will have a
MultiPixelPackedSampleModel
and an
IndexColorModel
. For palette color images, the
ColorModel
palette contains the red, green, blue, and
optionally alpha palette information. For grayscale images, the
palette is used to expand the pixel data to cover the range 0-255.
The pixels are stored packed 8, 4, or 2 to the byte.
All other images are stored using a
PixelInterleavedSampleModel
with each sample of a pixel
occupying its own byte
or short
within
the DataBuffer
. A ComponentColorModel
is
used which simply extracts the red, green, blue, gray, and/or alpha
information from separate DataBuffer
entries.
Five aspects of this process may be altered by means of methods in this class.
setSuppressAlpha()
prevents an alpha channel
from appearing in the output.
setExpandPalette()
turns palette-color images into
3-or 4-channel full-color images.
setOutput8BitGray()
causes 1, 2, or 4 bit
grayscale images to be output in 8-bit form, using a
ComponentSampleModel
and
ComponentColorModel
.
setDecodingExponent()
causes the output image to be
gamma-corrected using a supplied output gamma value.
setExpandGrayAlpha()
causes 2-channel gray/alpha
(GA) images to be output as full-color (GGGA) images, which may
simplify further processing and display.
This class is not a committed part of the JAI API. It may be removed or changed in future releases of JAI.
| Constructor Summary | |
|---|---|
PNGDecodeParam()
Constructs a default instance of PNGDecodeParam . |
| Method Summary | |
|---|---|
float |
getDisplayExponent()
Returns the current value of the display exponent parameter. |
PNGEncodeParam |
getEncodeParam()
If getGenerateEncodeParam() is true ,
this method may be called after decoding has completed, and
will return an instance of PNGEncodeParam containing
information about the contents of the PNG file just decoded. |
boolean |
getExpandGrayAlpha()
Returns the current setting of the gray/alpha expansion. |
boolean |
getExpandPalette()
Returns true if palette-color images will be expanded to produce full-color output. |
boolean |
getGenerateEncodeParam()
Returns true if an instance of
PNGEncodeParam will be available after an image
has been decoded via the getEncodeParam method. |
boolean |
getOutput8BitGray()
Returns the current value of the 8-bit gray output parameter. |
boolean |
getPerformGammaCorrection()
Returns true if gamma correction is to be performed
on the image data. |
boolean |
getSuppressAlpha()
Returns true if alpha (transparency) will
be prevented from appearing in the output. |
float |
getUserExponent()
Returns the current value of the user exponent parameter. |
void |
setDisplayExponent(float displayExponent)
Sets the display exponent to a given value. |
void |
setEncodeParam(PNGEncodeParam encodeParam)
Sets the current encoder param instance. |
void |
setExpandGrayAlpha(boolean expandGrayAlpha)
If set, images containing one channel of gray and one channel of alpha (GA) will be output in a 4-channel format (GGGA). |
void |
setExpandPalette(boolean expandPalette)
If set, palette color images (PNG color type 3) will be decoded into full-color (RGB) output images. |
void |
setGenerateEncodeParam(boolean generateEncodeParam)
If set, an instance of PNGEncodeParam will be
available after an image has been decoded via the
getEncodeParam method that encapsulates information
about the contents of the PNG file. |
void |
setOutput8BitGray(boolean output8BitGray)
If set, grayscale images with a bit depth less than 8 (1, 2, or 4) will be output in 8 bit form. |
void |
setPerformGammaCorrection(boolean performGammaCorrection)
Turns gamma corection of the image data on or off. |
void |
setSuppressAlpha(boolean suppressAlpha)
If set, no alpha (transparency) channel will appear in the output image. |
void |
setUserExponent(float userExponent)
Sets the user exponent to a given value. |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public PNGDecodeParam()
PNGDecodeParam
.
| Method Detail |
|---|
public boolean getSuppressAlpha()
true
if alpha (transparency) will
be prevented from appearing in the output.
public void setSuppressAlpha(boolean suppressAlpha)
The default is to allow transparency to appear in the output image.
public boolean getExpandPalette()
public void setExpandPalette(boolean expandPalette)
The default is to output palette images using a single
channel. The palette information is used to construct the
output image's ColorModel
.
public boolean getOutput8BitGray()
public void setOutput8BitGray(boolean output8BitGray)
The decoding of non-grayscale images and grayscale images with a bit depth of 8 or 16 are unaffected by this setting.
The default is not to perform expansion. Grayscale images
with a depth of 1, 2, or 4 bits will be represented using
a MultiPixelPackedSampleModel
and an
IndexColorModel
.
public boolean getPerformGammaCorrection()
true
if gamma correction is to be performed
on the image data. The default is true
.
If gamma correction is to be performed, the
getUserExponent()
and
getDisplayExponent()
methods are used in addition to
the gamma value stored within the file (or the default value of
1/2.2 used if no value is found) to produce a single exponent
using the formula:
decoding_exponent = user_exponent/(gamma_from_file * display_exponent)
public void setPerformGammaCorrection(boolean performGammaCorrection)
public float getUserExponent()
public void setUserExponent(float userExponent)
IllegalArgumentException
will be thrown.
The output image pixels will be placed through a transformation of the form:
sample = integer_sample / (2^bitdepth - 1.0) decoding_exponent = user_exponent/(gamma_from_file * display_exponent) output = sample ^ decoding_exponentwhere
gamma_from_file
is the gamma of the file
data, as determined by the gAMA
, sRGB,
and/or iCCP
chunks, and display_exponent
is the exponent of the intrinsic transfer curve of the display,
generally 2.2.
Input files which do not specify any gamma are assumed to
have a gamma of 1/2.2
; such images may be displayed
on a CRT with an exponent of 2.2 using the default user
exponent of 1.0.
The user exponent may be used in order to change the effective gamma of a file. If a file has a stored gamma of X, but the decoder believes that the true file gamma is Y, setting a user exponent of Y/X will produce the same result as changing the file gamma.
This parameter affects the decoding of all image types.
java.lang.IllegalArgumentException
- if userExponent
is
negative.public float getDisplayExponent()
public void setDisplayExponent(float displayExponent)
IllegalArgumentException
will be thrown.
The output image pixels will be placed through a transformation of the form:
sample = integer_sample / (2^bitdepth - 1.0) decoding_exponent = user_exponent/(gamma_from_file * display_exponent) output = sample ^ decoding_exponentwhere
gamma_from_file
is the gamma of the file
data, as determined by the gAMA
, sRGB,
and/or iCCP
chunks, and user_exponent
is an additional user-supplied parameter.
Input files which do not specify any gamma are assumed to
have a gamma of 1/2.2
; such images should be
decoding using the default display exponent of 2.2.
If an image is to be processed further before being displayed, it may be preferable to set the display exponent to 1.0 in order to produce a linear output image.
This parameter affects the decoding of all image types.
java.lang.IllegalArgumentException
- if userExponent
is
negative.public boolean getExpandGrayAlpha()
public void setExpandGrayAlpha(boolean expandGrayAlpha)
This setting affects both images of color type 4 (explicit alpha) and images of color type 0 (grayscale) that contain transparency information.
By default, no expansion is performed.
public boolean getGenerateEncodeParam()
true
if an instance of
PNGEncodeParam
will be available after an image
has been decoded via the getEncodeParam
method.
public void setGenerateEncodeParam(boolean generateEncodeParam)
PNGEncodeParam
will be
available after an image has been decoded via the
getEncodeParam
method that encapsulates information
about the contents of the PNG file. If not set, this information
will not be recorded and getEncodeParam()
will
return null
.
public PNGEncodeParam getEncodeParam()
getGenerateEncodeParam()
is true
,
this method may be called after decoding has completed, and
will return an instance of PNGEncodeParam
containing
information about the contents of the PNG file just decoded.
public void setEncodeParam(PNGEncodeParam encodeParam)
getEncodeParam
.
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The responsive splitter layout structures complex applications into defined areas. These areas may be resizable and are either distributed on one screen or across different areas, which may also be off-canvas. This depends on the device class and the requirements and settings of the application.
The defined areas are resizable manually, programmatically, and/or by external function holders such as buttons or menu entries. These areas and their parent containers are parts of the responsive splitter control and have properties that can be changed to meet an application’s need.


| restarting {rlang} | R Documentation |
This constructor automates the common task of creating an
calling()
handler that invokes a restart.
restarting(.restart, ..., .fields = NULL)
.restart |
The name of a restart. |
... |
<dynamic> Additional arguments passed on the restart function. These arguments are evaluated only once and immediately, when creating the restarting handler. |
.fields |
A character vector specifying the fields of the
condition that should be passed as arguments to the restart. If
named, the names (except empty names |
Jumping to a restart point from a calling handler has two
effects. First, the control flow jumps to wherever the restart was
established, and the restart function is called (with ...
, or
.fields
as arguments). Execution resumes from the
with_restarts()
call. Secondly, the transfer of the control flow
out of the function that signalled the condition means that the
handler has dealt with the condition. Thus the condition will not
be passed on to other potential handlers established on the stack.
# This is a restart that takes a data frame and names as arguments rst_bar <- function(df, nms) { stats::setNames(df, nms) } # This restart is simpler and does not take arguments rst_baz <- function() "baz" # Signalling a condition parameterised with a data frame fn <- function() { with_restarts(signal("A foobar condition occurred", "foo", foo_field = mtcars), rst_bar = rst_bar, rst_baz = rst_baz ) } # Creating a restarting handler that passes arguments `nms` and # `df`, the latter taken from a data field of the condition object restart_bar <- restarting("rst_bar", nms = LETTERS[1:11], .fields = c(df = "foo_field") ) # The restarting handlers jumps to `rst_bar` when `foo` is signalled: with_handlers(fn(), foo = restart_bar) # The restarting() constructor is especially nice to use with # restarts that do not need arguments: with_handlers(fn(), foo = restarting("rst_baz"))


HOARenderer
is an optimized higher-order ambisonic renderer based on the integrated convolution (SH-MaxRe
HRIR). This renderer supports arbitrary order ambisonic rendering, provided the user supplies the appropriate SH-MaxRe
HRIRs.
NOTE: Use headphones for the full-sphere surround sound.


Blog
A selection of articles is available in this section.
I'm now on Instagram
Published 22 January 2018
I am now regularly publishing photos of my work on Instagram.
Read MoreA selection of articles is available in this section.
Published 22 January 2018
I am now regularly publishing photos of my work on Instagram.
Read More


In this area, configure a list of patterns that determine the lines to be hidden in console output. Any line that contains one of these patterns as a substring is hidden.


Neil deGrasse Tyson American astronomer
Neil deGrasse Tyson, (born October 5, 1958, New York, New York, U.S.), American astronomer who popularized science with his books and frequent appearances on radio and television. When Tyson was nine years old, his interest in astronomy was sparked by a trip to the Hayden Planetarium at the American Museum of Natural History in New York City. Tyson received a bachelor’s degree in physics from Harvard University in Cambridge, Massachusetts, in 1980 and a master’s degree in astronomy from the University of Texas at Austin in 1983. He began writing a question-and-answer column for the University of Texas’s popular astronomy magazine StarDate, and material from that column later appeared in his books Merlin’s Tour of the Universe (1989) and Just Visiting This Planet (1998).
Tyson then earned a master’s (1989) and a doctorate in astrophysics (1991)
Tyson then earned a master’s (1989) and a doctorate in astrophysics (1991) from Columbia University, New York City. He was a postdoctoral research associate at Princeton University from 1991 to 1994, when he joined the Hayden Planetarium as a staff scientist. His research dealt with problems relating to galactic structure and evolution. He became acting director of the Hayden Planetarium in 1995 and director in 1996. From 1995 to 2005 he wrote monthly essays for Natural History magazine, some of which were collected in Death by Black Hole: And Other Cosmic Quandaries (2007), and in 2000 he wrote an autobiography, The Sky Is Not the Limit: Adventures of an Urban Astrophysicist. His later books included Astrophysics for People in a Hurry (2017) and Letters from an Astrophysicist (2019).
As director of the Hayden Planetarium
As director of the Hayden Planetarium, Tyson oversaw a complete replacement of the facility, which opened in 2000. The new planetarium’s exhibit categorized the solar system’s bodies into groups. Pluto was not classified with either the terrestrial or Jovian planets but was grouped with the Kuiper belt objects. That decision (made six years before the International Astronomical Union designated Pluto as a dwarf planet) proved quite controversial, and Tyson was deluged with angry letters. He wrote about that experience in The Pluto Files: The Rise and Fall of America’s Favorite Planet (2009), in which he attributed some of the sentimental attachment to Pluto’s planethood to cultural factors such as Pluto being the only planet discovered by an American (astronomer Clyde Tombaugh) and having the popular cartoon character of Mickey Mouse’s dog named after it.


Here is your temperature conversion
Farehheit: {{ showFtemp }}
Celsius: {{ showCtemp }}
Do another conversion


#include <AproxERSLMJacobiCG.hpp>
|
| gSparse::COMPUTE_INFO | _calculateER (gSparse::PrecisionRowMatrix &er, const gSparse::Graph &graph, double eps=1.0f, double JLTol=0.5, int maxIter=300) |
| |
This class Approximate Effective Weight Resistance Adaptation from http://ccom.uprrp.edu/~ikoutis/SpectralAlgorithms.htm. The algorithm leverages Conjugated Graident with Jacobi preconditioner to solve linear system
◆ _calculateER()
| gSparse::COMPUTE_INFO gSparse::ER::Policy::AproxERSLMJacobiCG::_calculateER |
( |
gSparse::PrecisionRowMatrix & |
er, |
|
|
const gSparse::Graph & |
graph, |
|
|
double |
eps = 1.0f , |
|
|
double |
JLTol = 0.5 , |
|
|
int |
maxIter = 300 |
|
) |
| |
|
inlineprotected |
This function calculates Effective Resistance and return computation status.
- Parameters
-
| er | A row matrix to receive the EffectiveResistance value |
| graph | A std::shared_ptr<IGraph> object representing the graph to calculate resistance |
| eps | Error tolerance for conjugated gradient. Default is 1.0f. |
| JLTol | Tolerance for JL projection Matrix. Default is 0.5f. (See http://ccom.uprrp.edu/~ikoutis/SpectralAlgorithms.htm.) |
| maxIter | Maximum iteration for conjugated gradient. Default is 300 iterations. |
The documentation for this class was generated from the following file:


khome / khome / KhomeApplication / emitEvent
emitEvent
abstract fun emitEvent(eventType: String, eventData: Any? = null): Unit
Emits a home assistant event with optional event data.
Parameters
eventType
- the type of event to emit.
eventData
- the data to be send with the event (optional).


SPANISH PRISONER, THE (director/writer: David Mamet; cinematographer: Gabriel Beristain; cast: Campbell Scott (Joe Ross), Rebecca Pidgeon (Susan Ricci), Steve Martin (Jimmy Dell), Ben Gazzara (Klein), Felicity Huffman (Pat McCune), Ricky Jay (George Lang), 1998)
Reviewed by Dennis Schwartz
A great labyrinth plot, ala Hitchcock, as Mamet delves into the greedy corporate world and its nasty secrets of espionage and bitter competition, and dangles this world in front of us, with all its perks and glitter and hopes for money being the big payoff for these corporate types.  The chink in the story's armor is, when you see how it ends, you realize it doesn't add up. The fun seems to be in the telling of the story and not in the story itself making sense.
This is a masterfully crafted thriller for the '90s and its new values and lingo. Steve Martin (that's right, pal) steals the film with his stellar performance mixing affable charm and dark mystery to hide his persona. The film's theme incorporates how corrupting the corporate world is for a boy scout like Joe Ross (Campbell), who invented 'The Process,' and just wants what is his, what he earned by the sweat of his brow. This something, 'The Process,' is what should put him figuratively on 'easy street.' 'The Process' is a McGuffin, a Hitchcock invented term, it is something to focus your attention on without knowing what it really is. Its explanation for the movie is that it has something to do with a formula that can control the global market. But what Mamet really is showing, is greed and how that poisonous desire makes the world spin. This is pointed out when the board members of a company want to know how much they will earn from investing in it and Joe writes the supposed astronomical figure on a blackboard, which the audience is not privileged to see but is supposed to be impressed by it through the response taken from watching the widening eyes of the board members.
Joe's boss, Klein (Gazzara), plays him along without telling him how much he will receive for his invention, which is making Joe quite antsy. While on a business trip to a fictitious Caribbean island that is supposed to be for the wealthy and Japanese honeymooners, Joe Ross meets a couple of folks on this island who will alter his life. One is a woman who says she is an F.B.I. agent (Felicity). The other is the mysterious rich man Jimmy Dell (Martin), who is seen departing from a boat and could have also come off a seaplane, when Joe takes a snapshot of him and the girl he is with. Dell offers to buy the camera for a $1,000, but when Joe just gives the camera to him and turns down the money, a friendship starts.
The story twists and turns, as Dell and Joe make plans to meet back in NYC, with the innocent hero dropping off a package to Dell's attractive sister, who Dell shows him a photo of to lure him into making sure they meet again. Joe is like those heroes in '50s noir films who find themselves alone, fighting both the system and the criminal world. After meeting with Dell and being duped into opening a Swiss bank account and being made a member of a phony diner club, and having his world turned upside down, as he gets involved in things that he doesn't understand, he is left confused and unsure of what viable choices he has left, as his pride is hurt that he has been taken for a sucker. He is being wrongly accused of a crime and not being able to find anyone to trust, except for his friend and company lawyer, Ricky (Lang). But he is found dead in his room, leaving Joe once again isolated and afraid. This pulls him very close to the audience, as they identify with him and his situation, even if it is hardly possible that they themselves would ever be in such a position.
This is the kind of hero that speaks realistically to what could happen to anyone in today's work place, especially to someone who doesn't play the corporate game, and who doesn't know that the first axiom in business for the dishonest types, is always do business as if someone is trying to screw you. So, when he reluctantly teams up with Susan Ricci (Rebecca Pidgeon, Mamet's wife), who is the new secretary in his firm, the audience can understand this weakness in him, which is viewed as the kindness he shows to others. This stance he has, as the outsider who made good in a world where the odds are stacked against him, but is now having the rug pulled out from under him, makes him begin to be uncertain of everything, as he begins to lose trust in people. And as he wants to be a believer in people, he is drawn to Ricci, seeing in her someone who has a crush on him, and even though he is not attracted to her, he needs to confide in someone in whom he can trust and she seems to be giving him sound advice.
There is no point in giving away the story; that is, except to mention that "The Spanish Prisoner" refers to a scam. Mamet tells us it is the oldest scam in the books. In this film, it has nothing to do with Spain or a prisoner.
This tale is more frightening than a hundred action films put together. It is eerie because the innocent hero asks, during one of his low points, "Why steal what others worked for?" Mamet offers a naive plea for the corrupting influences of big business to stop their venal behavior, as he wonders why those who play fair always get eaten up by those who don't.
It is a film that earnestly asks, `Why is it that nobody in this world is what they seem?'
And that is a question we can all relate to in one way or another, as we are led along the same blind alleys Joe is, seeing the same things he sees, wondering if this Kafkaesque nightmare has a light at the end of the tunnel.
Has Mamet really ended the film by tying up all the loose ends and leaving us with the kind of ending we as an audience wanted?
And the answer is... Maybe. This is a film for people who like to use their noodle and are able to appreciate films that can't be tidied up so easily. Even the so-called tidy ending here might not be as tidy as it first appears, if you look carefully you will spot some loose ends.
REVIEWED ON 8/2/98 GRADE: B
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
<EMAIL_ADDRESS>© ALL RIGHTS RESERVED DENNIS SCHWARTZ
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rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
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belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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Macbook Pro - The Macbook Pro has met every need I’ve had. I miss the expandable memory and storage of the older models though. I’m not sold on the new version of the Macbook Pro. I don’t think I could go back to Windows full-time at this point though, although I do keep threatening to switch to a linux environment.
iTerm2 - iTerm2 version 3 has many features that are of great use while programming. Features I use regularly include split panes (cmd-d and cmd-shift-d), cmd-click filenames to edit that file, and the paste history. There are more features than I can remember, and the app increases productivity almost instantly.
Zsh & Oh-my-zsh - Zsh improves upon bash in every way. Text completion is awesome. Path expansion improves navigation immeasurably. The improved command history functionality is also a huge time saver. Oh-my-zsh supports numerous plugins and themes for zsh.
Git - Git is an easy to learn version control system for tracking changes in files worked on by multiple people. It was created by Linus for developing Linux. It is distributed so every git directory is a full-fledged repo.
Github - Github has revolutionized the programming world. Nearly every major open source project is hosted on github.com. It has driven the adoption of git as the VCS to use. The feature set is copied on other sites such as Bitbucket, but there’s nothing quite as good as the original. It’s free to use as long as your project is open to the public.
Gist - Github’s little brother, a great site developed by the Github team to share snippets of code with full syntax highlighting and easy to use interface.
Ruby on Rails - Ruby is my current programming language of choice, with Rails being the default framework for web development in Ruby. It’s quick to get an app up and running and has matured to the point of having widespread support for whatever issues you run into.
Bootstrap - Bootstrap is the most popular web framework for building responsive projects. There are a variety of css themes but for prototyping using the default css is quick and easy.
Atom - Atom is a highly extensible IDE released by the team at Github. It works as well as anything I’ve used and requires less learning than vim.
Postman - Postman is a great tool for developing APIs. You can import and export saved API documentation and examples. The only feature I wish it had was the ability to sync with a git repo to share requests amongst a team.
Chrome - Google’s browser has a wide variety of plugins and a solid set of developer tools.
Stack Overflow - Stack Overflow has allowed people to find answers to questions they seek (about programming). Gone are the days of searching through forums for somebody with similar issues to yours. If you google an issue with a modern programming language, chances are the first dozen results are Stack Overflow questions and answers.
Spotify - While not related to programming, Spotify does provide music, which helps keep me from getting distracted by outside influences. Spotify is a great service and a great interface. The library is huge and ad-free service is worth the price of the subscription if used on a regular basis. The daily mixes are getting better and better to my taste and I’ve actually discovered numerous bands I like from the “Discover Weekly” playlist they generate.
Header photo taken by Clint Cecil at Yellowstone Lake in Yellowstone National Park, Wyoming.


Workflow generation
Edit (pull request)Default workflows are generated based on the following rules:
Install workflow
Node workflow
This is a graph showing default steps that a node must pass through in order to be instantiated in the default install workflow
Relationship workflow
This is a graph showing default steps that 2 nodes with relationship between them must pass through in order to be instantiated in the default install workflow
Uninstall workflow
Node workflow
This is a graph showing default steps that a node must pass through in order to be deleted in the default uninstall workflow
Relationship workflow
This is a graph showing default steps that 2 nodes with relationship between them must pass through in order to be deleted in the default uninstall workflow
Start workflow
Node workflow
This is a graph showing default steps that a node must pass through in order to be started in the default start workflow
Relationship workflow
This is a graph showing default steps that 2 nodes with relationship between them must pass through in order to be started in the default start workflow
Stop workflow
Node workflow
This is a graph showing default steps that a node must pass through in order to be stopped in the default stop workflow
Relationship workflow
This is a graph showing default steps that 2 nodes with relationship between them must pass through in order to be stopped in the default stop workflow
Edit (pull request)