a riddle

Nemesis is also another name for Nibiru, a hypothetical brown dwarf orbiting the sun at extreme distances. Still technically in Sol so it would fit with the Sol theme. I think Drew did confirm the event on the 29th will take place in the bubble. Polaris is permit locked and has been for quite a while. It would be cool if they opened it for the 29th.

Fibonacci's zephyrum/a poor misers sum also likely means zero. That could mean a point of origin or center if we look at a graphical representation of the Fibonacci sequence. That could mean alot of places - Sol, Prism, etc.

It's also possible that it means that the center of the spiral is where the chase is going to end up, so we've got to figure that out, instead of searching along the line that spirals out from the sequence.

Then again, that would mean that FDev is taking it easy on us...never mind.
 
The river to the underworld is most likely Acheron, as that is the river Charon ferries the dead over towards the underworld (the river Styx is a tribuary from the Acheron). Possibly also the river Lethe, which is also the name of a daughter of Gaia and as well one of the seven rivers circling the underworld.

The Fibanocci Zephyrum points towards a planet in our solar system with no moons (Fibonacci orders the planets by mooncount). This could be Venus or Mercury, but also one of the outlying exoplanets (which weren't known in the time of Fibonacci).

As for the bear in vain. Apparently the north star is pointless when one is close to the northern pole as its position no longer indicates direction. The innuit therefore used other stars to navigate. Polaris would be a good bet (or is it an indication to search the northern pole?)
 
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Maybe it's suggesting a playlist?

[video=youtube;e5MAg_yWsq8]https://www.youtube.com/watch?v=e5MAg_yWsq8&app=desktop[/video]
 
They're the name of planets

Here is the list;

1. Styx
2. Rhea
3. Yildun
4. Eurydice
5. Rhamnousia
6. Bellerophon
7. Epione
8. Eurycleia
9. Amphithea
10. Cronus
11. Daedalion
12. Chione
13. Iapetus
14. Anticlea
15. Aeolus
16. Erebus
17. Jupiter
18. Xanthus
19. Furies
20. Cassiopeia
21. Zero
 
A misers sum maybe?

Recursive stratified sampling[edit]


An illustration of Recursive Stratified Sampling. In this example, the function:
f ( x , y ) = { 1 x 2 + y 2 < 1 0 x 2 + y 2 ≥ 1 {\displaystyle f(x,y)={\begin{cases}1&x^{2}+y^{2}<1\\0&x^{2}+y^{2}\geq 1\end{cases}}}
from the above illustration was integrated within a unit square using the suggested algorithm. The sampled points were recorded and plotted. Clearly stratified sampling algorithm concentrates the points in the regions where the variation of the function is largest.
Recursive stratified sampling is a generalization of one-dimensional adaptive quadratures to multi-dimensional integrals. On each recursion step the integral and the error are estimated using a plain Monte Carlo algorithm. If the error estimate is larger than the required accuracy the integration volume is divided into sub-volumes and the procedure is recursively applied to sub-volumes.
The ordinary 'dividing by two' strategy does not work for multi-dimensions as the number of sub-volumes grows far too quickly to keep track. Instead one estimates along which dimension a subdivision should bring the most dividends and only subdivides the volume along this dimension.
The stratified sampling algorithm concentrates the sampling points in the regions where the variance of the function is largest thus reducing the grand variance and making the sampling more effective, as shown on the illustration.
The popular MISER routine implements a similar algorithm.
MISER Monte Carlo[edit]
The MISER algorithm is based on recursive stratified sampling. This technique aims to reduce the overall integration error by concentrating integration points in the regions of highest variance.[6]
The idea of stratified sampling begins with the observation that for two disjoint regions a and b with Monte Carlo estimates of the integral
E a ( f ) {\displaystyle E_{a}(f)}
and
E b ( f ) {\displaystyle E_{b}(f)}
and variances
σ a 2 ( f ) {\displaystyle \sigma _{a}^{2}(f)}
and
σ b 2 ( f ) {\displaystyle \sigma _{b}^{2}(f)}
, the variance Var(f) of the combined estimate
E ( f ) = 1 2 ( E a ( f ) + E b ( f ) ) {\displaystyle E(f)={\tfrac {1}{2}}\left(E_{a}(f)+E_{b}(f)\right)}

is given by,
V a r ( f ) = σ a 2 ( f ) 4 N a + σ b 2 ( f ) 4 N b {\displaystyle \mathrm {Var} (f)={\frac {\sigma _{a}^{2}(f)}{4N_{a}}}+{\frac {\sigma _{b}^{2}(f)}{4N_{b}}}}

It can be shown that this variance is minimized by distributing the points such that,
N a N a + N b = σ a σ a + σ b {\displaystyle {\frac {N_{a}}{N_{a}+N_{b}}}={\frac {\sigma _{a}}{\sigma _{a}+\sigma _{b}}}}

Hence the smallest error estimate is obtained by allocating sample points in proportion to the standard deviation of the function in each sub-region.
The MISER algorithm proceeds by bisecting the integration region along one coordinate axis to give two sub-regions at each step. The direction is chosen by examining all d possible bisections and selecting the one which will minimize the combined variance of the two sub-regions. The variance in the sub-regions is estimated by sampling with a fraction of the total number of points available to the current step. The same procedure is then repeated recursively for each of the two half-spaces from the best bisection. The remaining sample points are allocated to the sub-regions using the formula for Na and Nb. This recursive allocation of integration points continues down to a user-specified depth where each sub-region is integrated using a plain Monte Carlo estimate. These individual values and their error estimates are then combined upwards to give an overall result and an estimate of its error.

Courtesy of Wikipedia - https://en.wikipedia.org/wiki/Monte_Carlo_integration
A misers sum would surely not be zero at any rate, as it is a misers goal to spend as little as possible whilst amassing a large store.
 
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If this is a chase type event. Its pretty sneaky using systems that are rank locked. But it does give for a very large area for Salome to travel giving more cmdrs chances at interdictions or protections. But why a spiral path?
 
If this is a chase type event. Its pretty sneaky using systems that are rank locked. But it does give for a very large area for Salome to travel giving more cmdrs chances at interdictions or protections. But why a spiral path?

It's possible that she'll be trying to throw the cops off of her scent. Or she could be looking for certain things in each system, and going to them in sequence.
 
If this is a chase type event. Its pretty sneaky using systems that are rank locked. But it does give for a very large area for Salome to travel giving more cmdrs chances at interdictions or protections. But why a spiral path?
Due to the Fibonacci sequence of numbers. 1+2=3 2+3=5 3+5=8, 13, 21, 34, 55, 89, etc. etc. Now think of an A4 sheet of paper, the dimensions are worked out using the same maths. now think A5, A6, A7, A8, A9, but don't cut the paper smaller and smaller, just draw lines on the A4 paper. Now if you connect the opposite diagonal corners of each section with a curved line, it becomes a spiral. This same shape, is formed in nature in so many ways it is amazing; sea shells are prime example, the spiral ones are built up using the Fibonacci sequence.
 
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A misers sum maybe?

Recursive stratified sampling[edit]


An illustration of Recursive Stratified Sampling. In this example, the function:
f ( x , y ) = { 1 x 2 + y 2 < 1 0 x 2 + y 2 ≥ 1 {\displaystyle f(x,y)={\begin{cases}1&x^{2}+y^{2}<1\\0&x^{2}+y^{2}\geq 1\end{cases}}}
from the above illustration was integrated within a unit square using the suggested algorithm. The sampled points were recorded and plotted. Clearly stratified sampling algorithm concentrates the points in the regions where the variation of the function is largest.
Recursive stratified sampling is a generalization of one-dimensional adaptive quadratures to multi-dimensional integrals. On each recursion step the integral and the error are estimated using a plain Monte Carlo algorithm. If the error estimate is larger than the required accuracy the integration volume is divided into sub-volumes and the procedure is recursively applied to sub-volumes.
The ordinary 'dividing by two' strategy does not work for multi-dimensions as the number of sub-volumes grows far too quickly to keep track. Instead one estimates along which dimension a subdivision should bring the most dividends and only subdivides the volume along this dimension.
The stratified sampling algorithm concentrates the sampling points in the regions where the variance of the function is largest thus reducing the grand variance and making the sampling more effective, as shown on the illustration.
The popular MISER routine implements a similar algorithm.
MISER Monte Carlo[edit]
The MISER algorithm is based on recursive stratified sampling. This technique aims to reduce the overall integration error by concentrating integration points in the regions of highest variance.[6]
The idea of stratified sampling begins with the observation that for two disjoint regions a and b with Monte Carlo estimates of the integral
E a ( f ) {\displaystyle E_{a}(f)}
and
E b ( f ) {\displaystyle E_{b}(f)}
and variances
σ a 2 ( f ) {\displaystyle \sigma _{a}^{2}(f)}
and
σ b 2 ( f ) {\displaystyle \sigma _{b}^{2}(f)}
, the variance Var(f) of the combined estimate
E ( f ) = 1 2 ( E a ( f ) + E b ( f ) ) {\displaystyle E(f)={\tfrac {1}{2}}\left(E_{a}(f)+E_{b}(f)\right)}

is given by,
V a r ( f ) = σ a 2 ( f ) 4 N a + σ b 2 ( f ) 4 N b {\displaystyle \mathrm {Var} (f)={\frac {\sigma _{a}^{2}(f)}{4N_{a}}}+{\frac {\sigma _{b}^{2}(f)}{4N_{b}}}}

It can be shown that this variance is minimized by distributing the points such that,
N a N a + N b = σ a σ a + σ b {\displaystyle {\frac {N_{a}}{N_{a}+N_{b}}}={\frac {\sigma _{a}}{\sigma _{a}+\sigma _{b}}}}

Hence the smallest error estimate is obtained by allocating sample points in proportion to the standard deviation of the function in each sub-region.
The MISER algorithm proceeds by bisecting the integration region along one coordinate axis to give two sub-regions at each step. The direction is chosen by examining all d possible bisections and selecting the one which will minimize the combined variance of the two sub-regions. The variance in the sub-regions is estimated by sampling with a fraction of the total number of points available to the current step. The same procedure is then repeated recursively for each of the two half-spaces from the best bisection. The remaining sample points are allocated to the sub-regions using the formula for Na and Nb. This recursive allocation of integration points continues down to a user-specified depth where each sub-region is integrated using a plain Monte Carlo estimate. These individual values and their error estimates are then combined upwards to give an overall result and an estimate of its error.

Courtesy of Wikipedia - https://en.wikipedia.org/wiki/Monte_Carlo_integration
A misers sum would surely not be zero at any rate, as it is a misers goal to spend as little as possible whilst amassing a large store.

Wot he said ^^

[where is it]

I failed everything except for the ability to run up big hills with a 75lb Bergen full of socks and packets of boil in the bag chocolate and peaches, explosives, plus a weapon and ammo.

I could also aim said weapon effectively, blow stuff up with PE4 resulting in a huge BANG! hopefully not killing myself or my mates before running back down the hill again without getting shot at and upsetting my parents... :)
 
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A misers sum maybe?

Recursive stratified sampling[edit]


An illustration of Recursive Stratified Sampling. In this example, the function:
f ( x , y ) = { 1 x 2 + y 2 < 1 0 x 2 + y 2 ≥ 1 {\displaystyle f(x,y)={\begin{cases}1&x^{2}+y^{2}<1\\0&x^{2}+y^{2}\geq 1\end{cases}}}
from the above illustration was integrated within a unit square using the suggested algorithm. The sampled points were recorded and plotted. Clearly stratified sampling algorithm concentrates the points in the regions where the variation of the function is largest.
Recursive stratified sampling is a generalization of one-dimensional adaptive quadratures to multi-dimensional integrals. On each recursion step the integral and the error are estimated using a plain Monte Carlo algorithm. If the error estimate is larger than the required accuracy the integration volume is divided into sub-volumes and the procedure is recursively applied to sub-volumes.
The ordinary 'dividing by two' strategy does not work for multi-dimensions as the number of sub-volumes grows far too quickly to keep track. Instead one estimates along which dimension a subdivision should bring the most dividends and only subdivides the volume along this dimension.
The stratified sampling algorithm concentrates the sampling points in the regions where the variance of the function is largest thus reducing the grand variance and making the sampling more effective, as shown on the illustration.
The popular MISER routine implements a similar algorithm.
MISER Monte Carlo[edit]
The MISER algorithm is based on recursive stratified sampling. This technique aims to reduce the overall integration error by concentrating integration points in the regions of highest variance.[6]
The idea of stratified sampling begins with the observation that for two disjoint regions a and b with Monte Carlo estimates of the integral
E a ( f ) {\displaystyle E_{a}(f)}
and
E b ( f ) {\displaystyle E_{b}(f)}
and variances
σ a 2 ( f ) {\displaystyle \sigma _{a}^{2}(f)}
and
σ b 2 ( f ) {\displaystyle \sigma _{b}^{2}(f)}
, the variance Var(f) of the combined estimate
E ( f ) = 1 2 ( E a ( f ) + E b ( f ) ) {\displaystyle E(f)={\tfrac {1}{2}}\left(E_{a}(f)+E_{b}(f)\right)}

is given by,
V a r ( f ) = σ a 2 ( f ) 4 N a + σ b 2 ( f ) 4 N b {\displaystyle \mathrm {Var} (f)={\frac {\sigma _{a}^{2}(f)}{4N_{a}}}+{\frac {\sigma _{b}^{2}(f)}{4N_{b}}}}

It can be shown that this variance is minimized by distributing the points such that,
N a N a + N b = σ a σ a + σ b {\displaystyle {\frac {N_{a}}{N_{a}+N_{b}}}={\frac {\sigma _{a}}{\sigma _{a}+\sigma _{b}}}}

Hence the smallest error estimate is obtained by allocating sample points in proportion to the standard deviation of the function in each sub-region.
The MISER algorithm proceeds by bisecting the integration region along one coordinate axis to give two sub-regions at each step. The direction is chosen by examining all d possible bisections and selecting the one which will minimize the combined variance of the two sub-regions. The variance in the sub-regions is estimated by sampling with a fraction of the total number of points available to the current step. The same procedure is then repeated recursively for each of the two half-spaces from the best bisection. The remaining sample points are allocated to the sub-regions using the formula for Na and Nb. This recursive allocation of integration points continues down to a user-specified depth where each sub-region is integrated using a plain Monte Carlo estimate. These individual values and their error estimates are then combined upwards to give an overall result and an estimate of its error.

Courtesy of Wikipedia - https://en.wikipedia.org/wiki/Monte_Carlo_integration
A misers sum would surely not be zero at any rate, as it is a misers goal to spend as little as possible whilst amassing a large store.

Me no like brain pain..........
 
Due to the Fibonacci sequence of numbers. 1+2=3 2+3=5 3+5=8, 13, 21, 34, 55, 89, etc. etc. Now think of an A4 sheet of paper, the dimensions are worked out using the same maths. now think A5, A6, A7, A8, A9, but don't cut the paper smaller and smaller, just draw lines on the A4 paper. Now if you connect the opposite diagonal corners of each section with a curved line, it becomes a spiral. This same shape, is formed in nature in so many ways it is amazing; sea shells are prime example, the spiral ones are built up using the Fibonacci sequence.

Agree'd. But looking for more of an answer along these lines??? Or is the names being listed, actual way points to various systems, with key names being markers for alignments?

[video=youtube_share;sNH4RYT5HOE]https://youtu.be/sNH4RYT5HOE[/video]
 
So after consulting with a friend of mine, this is what we came up with:

The river to the underworld: Styx, Phlegethon, Acheron, Lethe, or Cocytus
Gaia’s daughter all unfurled: Rhea?
Fourth minor bear in vain: Ursa Minor?
By viper’s sting was slain : ?
Also known as Nemesis: Rhamnousia/ Rhamnusia or Adrasteia/ Adrestia
The doom of the Chimera : Beleraphon
She is tasked with soothing pain: Artemis?
Suckling Odysseus from afar: Athena?
Mother of Mother of: Hemera
Leader of the Titans : Cronos
He Transformed into a Hawk: Daedalion or Apollo
A daughter of Daedalion’s: Chion
The Piercer was how he was known: Cupid/Eros (maybe? Weren't sure at all on this one.)
Mother of Ulysses: Anticlea
Ruler of the winds: Zephyrum?
God of night, primordial flees: Nyx or Erebus
Zeus’ namesake now lies in Sol: Jupiter
Achilles’ favored horse: Balius and Xanthus, also possibly Pedasos
Women of Vengence infernal: Furies
The vain queen rides not forth: Cassiopiea
A final word, a course to follow, a poor miser’s sum: Meaning that the target is at the further end of the spiral, not the heart of it?
If you would understand it all, seek Fibonacci’s Zephyrum: Suggesting the spiral that's been brought up in this thread before?


I've got nothing for "By viper's sting was slain."

Other than that, that's what I've come up with. I'm not in any way suggesting that this is an exhaustive list.
 
So after consulting with a friend of mine, this is what we came up with:

The river to the underworld: Styx, Phlegethon, Acheron, Lethe, or Cocytus
Gaia’s daughter all unfurled: Rhea?
Fourth minor bear in vain: Ursa Minor?
By viper’s sting was slain : ?
Also known as Nemesis: Rhamnousia/ Rhamnusia or Adrasteia/ Adrestia
The doom of the Chimera : Beleraphon
She is tasked with soothing pain: Artemis?
Suckling Odysseus from afar: Athena?
Mother of Mother of: Hemera
Leader of the Titans : Cronos
He Transformed into a Hawk: Daedalion or Apollo
A daughter of Daedalion’s: Chion
The Piercer was how he was known: Cupid/Eros (maybe? Weren't sure at all on this one.)
Mother of Ulysses: Anticlea
Ruler of the winds: Zephyrum?
God of night, primordial flees: Nyx or Erebus
Zeus’ namesake now lies in Sol: Jupiter
Achilles’ favored horse: Balius and Xanthus, also possibly Pedasos
Women of Vengence infernal: Furies
The vain queen rides not forth: Cassiopiea
A final word, a course to follow, a poor miser’s sum: Meaning that the target is at the further end of the spiral, not the heart of it?
If you would understand it all, seek Fibonacci’s Zephyrum: Suggesting the spiral that's been brought up in this thread before?


I've got nothing for "By viper's sting was slain."

Other than that, that's what I've come up with. I'm not in any way suggesting that this is an exhaustive list.

As has been mentioned already. "By Viper's sting was slain." Is, or was, Cleopatra... not really with the general theme of Greek Mythology, but there you go.

- - - Updated - - -

Sometimes when we touch, the honestys too much.....

"And I have to close my eyes and hide..." :)
 
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Fourth minor bear in vain
I immediately thought "Ursa Minor", but According to Wikipedia, there is no "Delta Ursae Minoris". It goes Polaris, Beta, Gamma, Epsilon.
Which kinda verifies the "in vain" part.
 
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