OpenLLM-France/wikimedia huggingface dataset:
# Free Knowledge Culture Calendar/August 5

Today in 2009 Google’s acquisition of On2 got announced. After Google promptly gave On2’s top product VP8 to the general public, this proved to be only the first big step in a broader push to break the dominance of patent-encumbered media formats.
On2 was the provider of the main competing formats to MPEG’s lossy video compression standards. The company had previously contributed to the development of patent-free, open-source video compression (leading to OggTheora). Ever since the first MP3 patent infringement letters of 1998, there had been growing discontent with the patent licensing system for MPEG formats that restricted business models involving online media. As a result of particularly brazen royalty demands for the successor to the highly successful MPEG-4 AVC aka. H.264, much of the video coding industry rallied to the cause, culminating in the formation of the Alliance for Open Media consortium and the development of AV1.
← previous day · following day →
# Free Knowledge Culture Calendar/December 11

Today in 1989 Dr. Joseph Popp sent diskettes containing the AIDS Trojan, the first known ransomware, to thousands of recipients.
← previous day · following day →
# Free Knowledge Culture Calendar/December 12

Today in 1984 Cisco was founded. It became a multi-billion dollar company based on the production of physical infrastructure for the Internet.
← previous day · following day →
# Engineering Tables/Normal Distribution/Far-Right Tail Probability Content

Table of probability content between z and +∞ in the standardised normal distribution Z~N(0,1) for z ≥ 2 :
| z | P(Z>z) | z | P(Z>z) | z | P(Z>z) | z | P(Z>z) |
| 2.0 | 0.02275 | 3.0 | 0.001350 | 4.0 | 0.00003167 | 5.0 | 2.867 E-7 |
| 2.1 | 0.01786 | 3.1 | 0.0009676 | 4.1 | 0.00002066 | 5.5 | 1.899 E-8 |
| 2.2 | 0.01390 | 3.2 | 0.0006871 | 4.2 | 0.00001335 | 6.0 | 9.866 E-10 |
| 2.3 | 0.01072 | 3.3 | 0.0004834 | 4.3 | 0.00000854 | 6.5 | 4.016 E-11 |
| 2.4 | 0.00820 | 3.4 | 0.0003369 | 4.4 | 0.000005413 | 7.0 | 1.280 E-12 |
| 2.5 | 0.00621 | 3.5 | 0.0002326 | 4.5 | 0.000003398 | 7.5 | 3.191 E-14 |
| 2.6 | 0.004661 | 3.6 | 0.0001591 | 4.6 | 0.000002112 | 8.0 | 6.221 E-16 |
| 2.7 | 0.003467 | 3.7 | 0.0001078 | 4.7 | 0.000001300 | 8.5 | 9.480 E-18 |
| 2.8 | 0.002555 | 3.8 | 0.00007235 | 4.8 | 7.933 E-7 | 9.0 | 1.129 E-19 |
| 2.9 | 0.001866 | 3.9 | 0.00004810 | 4.9 | 4.792 E-7 | 9.5 | 1.049 E-21 |
| See a graph of the tail function here. |||||||||||
| This box: view • talk • |||||||||||
# Engineering Tables/Hilbert Transform Table

| | Signal Name | Signal $s(t)\,$ | Hilbert transform ${\mathcal {H}}\{s\}(t)$ |
| 1 | Sine | $\sin(t)\,$ | $-\cos(t)\,$ |
| 2 | Cosine | $\cos(t)\,$ | $\sin(t)\,$ |
| 3 | | $1 \over t^{2}+1$ | $t \over t^{2}+1$ |
| 4 | Sinc function | $\sin(t) \over t$ | $1-\cos(t) \over t$ |
| 5 | Rectangular function | $\sqcap (t)$ | ${1 \over \pi }\ln \left|{t+{1 \over 2} \over t-{1 \over 2}}\right|$ |
| 6 | Dirac delta function | $\delta (t)\,$ | ${1 \over \pi t}$ |
| 7 | Indicator function | $\chi _{[a,b]}(x)\,$ | ${\frac {1}{\pi }}\log \left\vert {\frac {x-a}{x-b}}\right\vert \,$ |
# Engineering Tables/IT Grades

| | Measuring Tools | Measuring Tools | Measuring Tools | Measuring Tools | Measuring Tools | Measuring Tools | Measuring Tools | Measuring Tools | Measuring Tools | Material | Material | Material | Material | Material | Material | Material | | |
| IT Grade | 01 | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 |
| | | | | | | | Fits | Fits | Fits | Fits | Fits | Fits | Fits | Large Manufacturing Tolerances | Large Manufacturing Tolerances | Large Manufacturing Tolerances | Large Manufacturing Tolerances | Large Manufacturing Tolerances |
# Engineering Tables/Laplace Transform Properties

| Property | Definition |
| Linearity | ${\mathcal {L}}\left\{af(t)+bg(t)\right\}=aF(s)+bG(s)$ |
| Differentiation | ${\mathcal {L}}\{f'\}=s{\mathcal {L}}\{f\}-f(0^{-})$ ${\mathcal {L}}\{f''\}=s^{2}{\mathcal {L}}\{f\}-sf(0^{-})-f'(0^{-})$ ${\mathcal {L}}\left\{f^{(n)}\right\}=s^{n}{\mathcal {L}}\{f\}-s^{n-1}f(0^{-})-\cdots -f^{(n-1)}(0^{-})$ |
| Frequency Division | ${\mathcal {L}}\{tf(t)\}=-F'(s)$ ${\mathcal {L}}\{t^{n}f(t)\}=(-1)^{n}F^{(n)}(s)$ |
| Frequency Integration | ${\mathcal {L}}\left\{{\frac {f(t)}{t}}\right\}=\int _{s}^{\infty }F(\sigma )\,d\sigma$ |
| Time Integration | ${\mathcal {L}}\left\{\int _{0}^{t}f(\tau )\,d\tau \right\}={\mathcal {L}}\left\{u(t)*f(t)\right\}={1 \over s}F(s)$ |
| Scaling | ${\mathcal {L}}\left\{f(at)\right\}={1 \over a}F\left({s \over a}\right)$ |
| Initial value theorem | $f(0^{+})=\lim _{s\to \infty }{sF(s)}$ |
| Final value theorem | $f(\infty )=\lim _{s\to 0}{sF(s)}$ |
| Frequency Shifts | ${\mathcal {L}}\left\{e^{at}f(t)\right\}=F(s-a)$ ${\mathcal {L}}^{-1}\left\{F(s-a)\right\}=e^{at}f(t)$ |
| Time Shifts | ${\mathcal {L}}\left\{f(t-a)u(t-a)\right\}=e^{-as}F(s)$ ${\mathcal {L}}^{-1}\left\{e^{-as}F(s)\right\}=f(t-a)u(t-a)$ |
| Convolution Theorem | ${\mathcal {L}}\{f(t)*g(t)\}=F(s)G(s)$ |
Where:
    $f(t)={\mathcal {L}}^{-1}\{F(s)\}$
    $g(t)={\mathcal {L}}^{-1}\{G(s)\}$
    $s=\sigma +j\omega$
# Engineering Tables/Fourier Transform Table

| | Time Domain | Frequency Domain |
| | $x(t)={\mathcal {F}}^{-1}\left\{X(\omega )\right\}$ | $X(\omega )={\mathcal {F}}\left\{x(t)\right\}$ |
| 1 | $X(j\omega )=\int _{-\infty }^{\infty }x(t)e^{-j\omega t}dt$ | $x(t)={\frac {1}{2\pi }}\int _{-\infty }^{\infty }X(\omega )e^{j\omega t}d\omega$ |
| 2 | $1\,$ | $2\pi \delta (\omega )\,$ |
| 3 | $-0.5+u(t)\,$ | ${\frac {1}{j\omega }}\,$ |
| 4 | $\delta (t)\,$ | $1\,$ |
| 5 | $\delta (t-c)\,$ | $e^{-j\omega c}\,$ |
| 6 | $u(t)\,$ | $\pi \delta (\omega )+{\frac {1}{j\omega }}\,$ |
| 7 | $e^{-bt}u(t)\,(b>0)$ | ${\frac {1}{j\omega +b}}\,$ |
| 8 | $\cos \omega _{0}t\,$ | $\pi \left[\delta (\omega +\omega _{0})+\delta (\omega -\omega _{0})\right]\,$ |
| 9 | $\cos(\omega _{0}t+\theta )\,$ | $\pi \left[e^{-j\theta }\delta (\omega +\omega _{0})+e^{j\theta }\delta (\omega -\omega _{0})\right]\,$ |
| 10 | $\sin \omega _{0}t\,$ | $j\pi \left[\delta (\omega +\omega _{0})-\delta (\omega -\omega _{0})\right]\,$ |
| 11 | $\sin(\omega _{0}t+\theta )\,$ | $j\pi \left[e^{-j\theta }\delta (\omega +\omega _{0})-e^{j\theta }\delta (\omega -\omega _{0})\right]\,$ |
| 12 | ${\mbox{rect}}\left({\frac {t}{\tau }}\right)\,$ | $\tau {\mbox{sinc}}\left({\frac {\tau \omega }{2\pi }}\right)\,$ |
| 13 | $\tau {\mbox{sinc}}\left({\frac {\tau t}{2\pi }}\right)\,$ | $2\pi {\mbox{rect}}\left({\frac {\omega }{\tau }}\right)\,$ |
| 14 | $\left(1-{\frac {2|t|}{\tau }}\right){\mbox{rect}}\left({\frac {t}{\tau }}\right)\,$ | ${\frac {\tau }{2}}{\mbox{sinc}}^{2}\left({\frac {\tau \omega }{4\pi }}\right)\,$ |
| 15 | ${\frac {\tau }{2}}{\mbox{sinc}}^{2}\left({\frac {\tau t}{4\pi }}\right)\,$ | $2\pi \left(1-{\frac {2|\omega |}{\tau }}\right){\mbox{rect}}\left({\frac {\omega }{\tau }}\right)\,$ |
| 16 | $e^{-a|t|},\Re \{a\}>0\,$ | ${\frac {2a}{a^{2}+\omega ^{2}}}\,$ |
| Notes: | ${\mbox{sinc}}(x)=\sin(\pi x)/(\pi x)$ ${\mbox{rect}}\left({\frac {t}{\tau }}\right)$ is the rectangular pulse function of width $\tau$ $u(t)$ is the Heaviside step function $\delta (t)$ is the Dirac delta function | ${\mbox{sinc}}(x)=\sin(\pi x)/(\pi x)$ ${\mbox{rect}}\left({\frac {t}{\tau }}\right)$ is the rectangular pulse function of width $\tau$ $u(t)$ is the Heaviside step function $\delta (t)$ is the Dirac delta function |
| This box: view • talk • |||
# Engineering Tables/Normal Distribution

The normal distibution is an extremely important family of continuous probability distributions. It has applications in every engineering discipline. Each member of the family may be defined by two parameters, location and scale: the mean ("average", μ) and variance (standard deviation squared, σ$^2$) respectively.
The probability density function, or pdf, of the distribution is given by:
       $\varphi _{\mu ,\sigma ^{2}}(x)={\frac {1}{\sigma {\sqrt {2\pi }}}}\exp \left(-{\frac {(x-\mu )^{2}}{2\sigma ^{2}}}\right).$
The cumulative distribution function, or cdf, of the normal distribution is:
       $\Phi _{\mu ,\sigma ^{2}}(x)=\int _{-\infty }^{x}\varphi _{\mu ,\sigma ^{2}}(u)\,du={\frac {1}{\sigma {\sqrt {2\pi }}}}\int _{-\infty }^{x}\exp {\Bigl-{\frac {(u-\mu )^{2}}{2\sigma ^{2}}}\ {\Bigr )}\,du$
These functions are often impractical to evaluate quickly, and therefore tables of values are used to allow fast look-up of required data. The family of normal distributions is infinite in size, but all can be "normalised" to the case with mean of 0 and SD of 1:
    Given a normal distribution $X\sim N(\mu ,\sigma ^{2})\,$, the standardised normal distribution, Z, is:
       $Z={\frac {X-\mu }{\sigma }}\!\sim N(0,1)$
Due to this relationship, all tables refer to the standardised distribution, Z.

## Tables
### Tables of CDF Values

* Probability Content from –∞ to Z
* Probability Content from –∞ to Z (Z≥0)
* Probability Content from –∞ to Z (Z≤0)
* Far-Right Tail Probability Content
* Tail Function Graph
# Famous Theorems of Mathematics/Number Theory/Fermat's Little Theorem
## Statement

If p is a rational prime, for all integers a ≠ 0,
          $a^{p-1}\equiv 1\mod {p}$

## Proofs

There are many proofs of Fermat's Little Theorem.
Proof 1 (Bijection)
Define a function $f(x)=ax$ (mod p). Let S={1,2,...,p-1} and T=f(S)={a,2a,...,(p-1)a}. We claim that these two sets are identical mod p.
Since all integers not equal to 0 have inverses mod p, for any integer m with 1≤m<p, $f(a^{-1}m)=m$. Then $f$ is surjective.
In addition, if $f(x)=f(y)$, then $ax\equiv ay$ and $a^{-1}ax\equiv x\equiv y\equiv a^{-1}ay$. Then $f$ is injective, and is bijective between S and T.
Then, mod p, the product of all of the elements of S will be equal to the product of elements of T, meaning that
          $\prod _{k=1}^{p-1}k\equiv \prod _{k=1}^{p-1}ak{\pmod {p}}$ and that
          $\prod _{k=1}^{p-1}k\equiv a^{p-1}\prod _{k=1}^{p-1}k{\pmod {p}}$.
Then
          $a^{p-1}\equiv 1\mod {p}$.
# Famous Theorems of Mathematics/Proof style

This is an example on how to design proofs. Another one is needed for definitions and axioms.

## Irrationality of the square root of 2

This result uses the following:
* Definition of rational number.
* Definition of prime and coprime.
* Definition of square root.
The square root of 2 is irrational, ${\sqrt {2}}\notin \mathbb {Q}$

## Proof

This is a proof by contradiction, so we assume that ${\sqrt {2}}\in \mathbb {Q}$ and hence ${\sqrt {2}}={\frac {a}{b}}$ for some a, b that are coprime.
This implies that $2={\frac {a^{2}}{b^{2}}}$. Rewriting this gives $2b^{2}=a^{2}\!\,$.
Since $b^{2}\in \mathbb {Z}$, we have that $2|a^{2}$. Since 2 is prime, 2 must be one of the prime factors of $a^{2}$, which are also the prime factors of $a$, thus, $2|a$.
So we may substitute a with $2k,k\in \mathbb {Z}$, and we have that $2b^{2}=4k^{2}\!\,$.
Dividing both sides with 2 yields $b^{2}=2k^{2}\!\,$, and using similar arguments as above, we conclude that $2|b$.
Here we have a contradiction; we assumed that a and b were coprime, but we have that $2|a$ and $2|b$.
Hence, the assumption was false, and ${\sqrt {2}}$ cannot be written as a rational number. Hence, it is irrational.

## Notes

* As a generalization one can show that the square root of every prime number is irrational.
* Another way to prove the same result is to show that $x^{2}-2$ is an irreducible polynomial in the field of rationals using Eisenstein's criterion.
# Famous Theorems of Mathematics/Number Theory/Prime Numbers

This page will contain proofs relating to prime numbers. Because the definitions are quite similar, proofs relating to irreducible numbers will also go on this page.

## Definition of Prime

A prime number p>1 is one whose only positive divisors are 1 and p.

## Basic results

Theorem: $p$ is prime and $p|ab$ implies that $p|a$ or $p|b$.
Proof: Let's assume that $p$ is prime and $p|ab$, and that $p\nmid a$. We must show that $p|b$.
Let's consider $\gcd(p,a)$. Because $p$ is prime, this can equal $1$ or $p$. Since $p\nmid a$ we know that $\gcd(p,a)=1$.
By the gcd-identity, $\gcd(p,a)=1=px+ay$ for some $x,y\in \mathbb {Z}$.
When this is multiplied by $b$ we arrive at $b=pbx+aby$.
Because $p|p$ and $p|ab$ we know that $p|(pbx+aby)$, and that $p|b$, as desired.
# Famous Theorems of Mathematics/Root of order n

For all $y>0$ and for all $n\in \mathbb {N}$ there exists $x\in \mathbb {R}$ such that $x^{n}=y$.

### Proof

Let us define a set $A={\Big \r\in \mathbb {R} :r\geq 0,r^{n}<y{\Big \}}$.
This set is non-empty (for $0\in A$) and has an upper-bound $y+1$ (for all $r>y+1$ we get $r^{n}>r>y$).
Therefore, by the completeness axiom of the real numbers it has a supremum $x$. We shall show that $x^{n}=y$.
* Suppose that $x^{n}<y$.
    It is sufficient to find $0<\varepsilon <1$ such that $(x+\varepsilon )^{n}<y$:
    ${\begin{aligned}(x+\varepsilon )^{n}&=\sum _{k\,=\,0}^{n}{\binom {n}{k}}x^{n-k}\varepsilon ^{k}\\&=x^{n}+\sum _{k\,=\,1}^{n}{\binom {n}{k}}x^{n-k}\varepsilon ^{k}\\&\leq x^{n}+\sum _{k\,=\,1}^{n}{\binom {n}{k}}x^{n-k}\varepsilon \qquad :\varepsilon ^{k}\leq \varepsilon \\&=x^{n}+\varepsilon \sum _{k\,=\,1}^{n}{\binom {n}{k}}x^{n-k}\\&=x^{n}+\varepsilon \left(\,\sum _{k\,=\,0}^{n}{\binom {n}{k}}x^{n-k}-x^{n}\right)\\&=x^{n}+\varepsilon {\bigl(x+1)^{n}-x^{n}{\bigr ]}<y\\\varepsilon &<\min \left\{1,{\frac {y-x^{n}}{(x+1)^{n}-x^{n}}}\right\}\end{aligned}}$
    hence $x+\varepsilon \in A$, but $x<x+\varepsilon$ and so $x+\varepsilon \notin A$. A contradiction.
* Suppose that $x^{n}>y$.
    As before, it is sufficient to find $0<\varepsilon <1$ such that $(x-\varepsilon )^{n}>y$:
    ${\begin{aligned}(x-\varepsilon )^{n}&=\sum _{k\,=\,0}^{n}{\binom {n}{k}}x^{n-k}(-\varepsilon )^{k}\\&=x^{n}+\sum _{k\,=\,1}^{n}{\binom {n}{k}}x^{n-k}(-\varepsilon )^{k}\\&\geq x^{n}+\sum _{k\,=\,1}^{n}{\binom {n}{k}}x^{n-k}(-\varepsilon )\qquad :(-\varepsilon )^{k}\geq -\varepsilon \\&=x^{n}-\varepsilon \sum _{k\,=\,1}^{n}{\binom {n}{k}}x^{n-k}\\&=x^{n}-\varepsilon \left(\,\sum _{k\,=\,0}^{n}{\binom {n}{k}}x^{n-k}-x^{n}\right)\\&=x^{n}-\varepsilon {\bigl(x+1)^{n}-x^{n}{\bigr ]}>y\\\varepsilon &<\min \left\{1,{\frac {x^{n}-y}{(x+1)^{n}-x^{n}}}\right\}\end{aligned}}$
    hence $x-\varepsilon \notin A$, but $x-\varepsilon <x$ and so $x-\varepsilon \in A$. A contradiction.
Therefore $x^{n}=y$.
$\blacksquare$
# Fundamentals of Transportation/Trip Generation/Homework
## Homework

* Keep a travel diary for the next week. Analyze your personal travel diary. Summarize the number of trips by mode, by purpose, by time of day, etc. To do so, for each trip record the following information
** the start and end time (to the nearest minute)
** start and end location of each trip,
** primary mode you took (drive alone, car driver with passenger, car passenger, bus, LRT, walk, bike, motorcycle, taxi, Zipcar, other). (use the codes provided)
** purpose (to work, return home, work related business, shopping, family/personal business, school, church, medical/dental, vacation, visit friends or relatives, other social recreational, other) (use the codes provided)
** if you traveled with anyone else, and if so whether they lived in your household or not.
Bonus: Email your professor at the end of everyday with a detailed log of your travel diary. (+5 points on the first exam)
# Fundamentals of Transportation/Trip Generation/Problem2

Modelers have estimated that the number of trips leaving Rivertown (T_O) is a function of the number of households (H) and the number of jobs (J), and the number of trips arriving in Marcytown (T_D) is also a function of the number of households and number of jobs.
$T_{O}=1H+0.1J;R^{2}=0.9\,\!$
$T_{D}=0.1H+1J;R^{2}=0.5\,\!$
Assuming all trips originate in Rivertown and are destined for Marcytown and:
Rivertown: 30000 H, 5000 J
Marcytown: 6000 H, 29000 J
Determine the number of trips originating in Rivertown and the number destined for Marcytown according to the model.
Which number of origins or destinations is more accurate? Why?
# Fundamentals of Transportation/Trip Generation/Problem

Planners have estimated the following models for the AM Peak Hour
$T_{O,i}=1.5*H_{i}\,\!$
$T_{D,j}=(1.5*E_{off,j})+(1*E_{oth,j})+(0.5*E_{ret,j})\,\!$
Where:
$T_{O,i}\,\!$ = Person Trips Originating in Zone $i\,\!$
$T_{D,j}\,\!$ = Person Trips Destined for Zone $j\,\!$
$H_{i}\,\!$ = Number of Households in Zone $i\,\!$
You are also given the following data
Data :
| Variable | Dakotopolis | New Fargo |
| $H$ | 10000 | 15000 |
| $E_{off}$ | 8000 | 10000 |
| $E_{oth}$ | 3000 | 5000 |
| $E_{ret}$ | 2000 | 1500 |
A. What are the number of person trips originating in and destined for each city?
B. Normalize the number of person trips so that the number of person trip origins = the number of person trip destinations. Assume the model for person trip origins is more accurate.
# Fundamentals of Transportation/Trip Generation/Problem3

Modelers have estimated that in the AM peak hour, the number of trip origins (T_O) is a function of the number of households (H) and the number of jobs (J), and the number of trip destinations (T_D) is also a function of the number of households and number of jobs.
$T_{O}=1.0H+0.1J;R^{2}=0.9\,\!$
$T_{D}=0.1H+1J;R^{2}=0.5\,\!$
Suburbia: 30000 H, 5000 J
Urbia: 6000 H, 29000 J
1) Determine the number of trips originating in and destined for Suburbia and for Urbia according to the model.
2) Does this result make sense? Normalize the result to improve its accuracy and sensibility?
3) If the interzonal travel cost (from Suburbia to Urbia or Urbia to Suburbia) is 10 minutes, and the intrazonal travel cost (trips within Suburbia or within Urbia) is 5 minutes, use a (doubly-constrained) gravity model wherein the impedance is $f(t_{ij})=t_{ij}^{-2}$, calculate the impedance, balance the matrix to match trip generation, and determine the trip table (within 2 percent accuracy).
# Fundamentals of Transportation/Trip Generation/Additional Problems
## Additional Problems

* Are number of destinations always less than origins?
* Pose 5 hypotheses about factors that affect work, non-work trips? How do these factors affect accuracy, and thus normalization?
* What is the acceptable level of error?
* Describe one variable used in trip generation and how it affects the model.
* What is the basic equation for normalization?
* Which of these models (home-end, work-end) are assumed to be more accurate? Why is it important to normalize trip generation models
* What are the different trip purposes/types trip generation?
* Why is it difficult to know who is traveling when?
* What share of trips during peak afternoon peak periods are work to home (>50%, <50%?), why?
* What does ORIO abbreviate?
* What types of employees (ORIO) are more likely to travel from work to home in the evening peak
* What does the trip rate tell us about various parts of the population?
* What does the “T-statistic” value tell us about the trip rate estimation?
* Why might afternoon work to home trips be more or less than morning home to work trips? Why might the percent of trips be different?
* Define frequency.
* Why do individuals > 65 years of age make fewer work to home trips?
* Solve the following problem. You have the following trip generation model:
$Trips=B_{1}Off+B_{2}Ind+B_{3}Ret$
And you are given the following coefficients derived from a regression model.
If there are 600 office employees, 300 industrial employees, and 200 retail employees, how many trips are going from work to home?
# EverQuest/Expansions/LDoN

please try to post only specific things, like progression paths, dialogues or faction issues.
* SOE link: http://everquest.station.sony.com/lostdungeons/
* LDoN merchant sorting script - lists all LDoN rewards (items, spells, augs etc.) - http://www.cardplace.com/eq/ldon/list.cgi
* LDoN traps: http://www.eqdiva.com/EQ/Guide.aspx?id=56&ForumID=132 and http://everquest.allakhazam.com/forum.html?forum=1;mid=1120565850841789717;num=22;page=1

## ldon adventure stone npcs and locations

Hail the NPC's listed below for an upgrade of your ADV stone after the number of wins listed below
Takish
* Ruanya Windleaf - North Ro - Adventure Camp (2)
* Farwein Windrun - South Ro - n2800 p500 (15 wins)
* Ruanya Windleaf - North Ro - Adventure Camp (30 wins)
* Nifel Faliwae - Greater Faydark - n1960 p2400 (50 wins)
* Ruanya Windleaf - North Ro - Adventure Camp (78 wins, End Charm)
Guk
* Selphra Giztral - South Ro - Adventure Camp (2 wins)
* Bealya Tanilsua - West Commonlands - n455 p2600 (15 wins)
* Selphra Giztral - South Ro - Adventure Camp (30 wins)
* Deblik Grumblok - Innothule Swamp - n1700 p1150 (50 wins)
* Selphra Giztral - South Ro - Adventure Camp (70 wins, End Charm)
Mistmoore Crypts
* Vual Stoutest - Butcherblock - Adventure Camp (2 wins)
* Henai Silentwalker - Dagnors Cauldron - n1660 n1230 (15 wins)
* Vual Stoutest - Butcherblock - Adventure Camp (30 wins)
* Elwinn Prelilaen - Lesser Faydark - p700 n580 (50 wins)
* Vual Stoutest - Butcherblock - Adventure Camp (76 wins, End Charm)
Rujarkan
* Barste Songweaver - East Commonlands Shady - Adventure Camp (2 wins)
* Ginehl Wiquar - West Freeport - n606 p180 (pok book - 15 wins)
* Barste Songweaver - East Commonlands Shady - Adventure Camp (30 wins)
* Shumpi Wimahnn - Highpass Hold (....) - p185 n20 (50 wins)
* Barste Songweaver - East Commonlands Shady - Adventure Camp (74 wins, End charm)
Miraguls
* Teria Grinntti - Everfrost - Adventure Camp (2 wins)
* Ubzial Iyeaql - Qeynos Hills - p3300 n500 (15 wins)
* Teria Grinntti - Everfrost - Adventure Camp (30 wins
* ? - 68 wins, End Charm)
# EverQuest/Races

* Barbarian
* Dark Elf
* Dwarf
* Erudite
* Froglok
* Gnome
* Half Elf
* Halfling
* High Elf
* Human
* Iksar
* Ogre
* Troll
* Vah Shir
* Wood Elf
# EverQuest/Races/Dark Elf

The epitome of evil, Dark elves start out in the underground city of Neriak. They are masters of magic, but also good with the sword. As such, they can play a Cleric, Enchanter, Magician, Necromancer, Rogue, Shadowknight, Warrior or Wizard. As an evil race, Dark Elves are generally killed on sight in most cities in Norrath, forcing then to sneak through areas patrolled by good guards. The dark race can see in the darkness as well as the light, and has the innate ability to hide from their foes. This is a difficult race to play, since you will have difficulty travelling through a large portion of Norrath without running into an NPC who wants to kill you.
Experience Modifier: ?
# EverQuest/Races/Barbarian

The barbarian starts off in the city of Halas, in the frozen Northlands. Not a very magical race, Barbarians can be Rogues, Shamen, Warriors, Beastlords, or Berserkers. Barbarians are a large race, similar to the troll and ogre, for the purposes of wearing armor, and get a fairly high starting strength and stamina. They are considered a good race for the purposes of most factions, so they can move freely throughout the good areas of Norrath.
Experience Modifier(+/-): - 5%