#TOTSS

[thetauntinghydra]

Tales of the Sanguine Sapphires: A WoF RP

━━━━⋅⋅⋅ˏˋ ✦ ˊˎ⋅⋅⋅━━━━ 

Are you ready to enter the world of magic…?

💠 Tales of the Sanguine Sapphires 💠

━━━━⋅⋅⋅ Plot Synopsis ⋅⋅⋅━━━━

300 years after the events of Scorching, in an era of shaky peace, magic arrives to Pyrrhia. 

Sanguine Sapphires, the first known Animus Artifat, is shattered, spilling its power into the hearts of hundreds of dragons.

These Sanguine Animi have been given strange, new magic. Magic which the world of Pyrrhia has never seen before…

Walk amongst friends and foes in a world filled with all ten tribes, join a faction, meet the Queens, and decide the fate of these new dragons... before they destroy everything.

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ˏˋ ✦ What we offer

Tales of the Sanguine Sapphires is a Wings of Fire roleplay years in the making! Unique lore, an ancient, unexplored setting, and a structured format allow for an easy and casual roleplay experience! We offer four different factions to join, the **Skywing Empire**, **the Kingdom of Night**, **the Hivewing Kingdom**, and the elusive **Talons of Magic**- each with their own tribal alliances and enemies. If that doesn't catch your eye, feel free to be **Factionless**, and carve your own path! *Below are just some highlights!*

    ✦ Extensive lore channels that change as you roleplay!

    ✦ NPCs and NPC databases!

    ✦ Location-specific channels!

    ✦ Mods are active and friendly roleplayers, just like you!

    ✦ Minimal tribe/ability restrictions and Semi-realism!

ˏˋ ✦ Beginner friendly!

If you're new to the world of Wings of Fire roleplays, or the books in general, you're in the right place! We’re open to beginners, either to the book series or roleplay itself! Additionally, our Tupperbot is easy to set up and operate!

Interested? That’s great! Check out the link below to join, and let the magic begin!

#wings of fire #wof oc #wof #wof rp #wings of fire rp #oc roleplay #roleplay #oc #TotSS #tales of the sanguine sapphires #artists on tumblr

[cubffections]

gud mornin' evrybodi ! happy happy tuesday , hope u all hav a luvlie day ! oh if u culd , tell mi ur fav bubble tea order :D need smthin new todai ໒꒰ྀིᵔ ᵕ ᵔ ꒱ྀི১

#ᶻ 𝘇 𐰁 ？ 𝓅𝒾𝓁𝓁𝑜𝓌𝓉𝒶𝓁𝓀𝓈 . . #on todai's agenda afta wrk . . doc's appt :c #nawt gna lie wz totss ignoring my appt bleh #but i've been sickly TT must check

[aroaceofthesea]

#bofff quant temps feia q no escoltava aquesta canço xd #canço explorium 2n any🥲 quins records #aiii els trobo molt a faltar a totss #mine #music #Spotify

[pinkplaycow]

Don't need to think, just need to play with your titties.

Don't need to think, just need to show off your titties.

Don't need to think, just need to jiggle your titties.

Don't need to think, just need to be a pair of tits.

Don't think, just grow.

Don't think, just grow.

Tits don't need to think.

dontt thinkk totss dont nred to thinkk just groww growing pops my brainn ngh toyuching my tittiesss

#nsft #breast expansion #hypnosis #ask ^o^ #free use nsft #hypnok1nk #brainwashing #hypno pet

[conceptncookie]

I’m not fan of #TOTSS because it feels like watching #TLW in a bizarre parallel universe, but I do love the aesthetics of 1910s South Pacific. So here’s David and Isabelle for you.

#marguerite krux #margueritekrux #the lost world #thelostworld #lord john roxton #lordjohnroxton #tottss

[jonroxton]

Hi, I rediscovered The Lost World this year and learned about Tales of the South Seas from you. Would you recommend TotSS? (Admitted got curious because of the Marguerite and Roxton vibes from the gifs...)

Yes, I would! It’s a great show and only has one season. The only thing tho is that Rachel Blakely’s character is recurring, so you’ll get a bunch of Marguerite/Roxton-esque scenes, but not as much as in The Lost World.

#mystuff #the lost world #tales of the south seas

[transdangernoodle]

Venom: We demand tator totss

Eddie: Dude eat else something for once

Venom: Fine

Venom: We demand dick

#marvel #mcu #veddie #symbrock #venom #eddie brock #venom movie #we are venom #incorrect mcu quotes #incorrect marvel quotes #incorrect quotes

[ruwiii]

sudden totss

do i have any silent reader? charot feeling peymus

[thetauntinghydra]

King Abispa for the TotSS RP

Original design by STORMTEETH

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Server Link (Discord)

Lore Info: docs.google.com/document/d/1Yr…

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#my ocs #oc art #art #digital art #oc #artists on tumblr #wings of fire #wings of fire rp #dragon art #dragon #hivewing

[dontworryitchuki]

TOTSS - Thoughts Of The Sinner and Sacred - More and More Problems

PROBLEMS... problems, problems, problems, everyday we have problems, after solving one there's another one or more coming in (like your Math teacher). In a Big Bang, our world is created and rooted with problems, souled with challenges.

Ironically we humans are problems in this world, we are the number one problem of Mother Earth. She cannot do anything or may she can do something. With her beauty, she will naturally remove lives using disasters, earthquakes, tsunamis, volcano eruptions, hurricanes.

What is my point here? Nothing, I'm just writing what I think. Does this piece helps? I don't know, if it does, great!

[ultragaurav]

Running a k-means Cluster Analysis

K-means algorithm can be summarized as follows:

Specify the number of clusters (K) to be created (by the analyst)

Select randomly k objects from the data set as the initial cluster centers or means

Assigns each observation to their closest centroid, based on the Euclidean distance between the object and the centroid

For each of the k clusters update the cluster centroid by calculating the new mean values of all the data points in the cluster. The centroid of a Kth cluster is a vector of length p containing the means of all variables for the observations in the kth cluster; p is the number of variables.

Iteratively minimize the total within sum of square (Eq. 7). That is, iterate steps 3 and 4 until the cluster assignments stop changing or the maximum number of iterations is reached. By default, the R software uses 10 as the default value for the maximum number of iterations.

Computing k-means clustering in R

We can compute k-means in R with the kmeans function. Here will group the data into two clusters (centers = 2). The kmeans function also has an nstart option that attempts multiple initial configurations and reports on the best one. For example, adding nstart = 25 will generate 25 initial configurations. This approach is often recommended.

k2 <- kmeans(df, centers = 2, nstart = 25) str(k2) ## List of 9 ##  $ cluster     : Named int [1:50] 1 1 1 2 1 1 2 2 1 1 ... ##   ..- attr(*, "names")= chr [1:50] "Alabama" "Alaska" "Arizona" "Arkansas" ... ##  $ centers     : num [1:2, 1:4] 1.005 -0.67 1.014 -0.676 0.198 ... ##   ..- attr(*, "dimnames")=List of 2 ##   .. ..$ : chr [1:2] "1" "2" ##   .. ..$ : chr [1:4] "Murder" "Assault" "UrbanPop" "Rape" ##  $ totss       : num 196 ##  $ withinss    : num [1:2] 46.7 56.1 ##  $ tot.withinss: num 103 ##  $ betweenss   : num 93.1 ##  $ size        : int [1:2] 20 30 ##  $ iter        : int 1 ##  $ ifault      : int 0 ##  - attr(*, "class")= chr "kmeans"

The output of kmeans is a list with several bits of information. The most important being:

cluster: A vector of integers (from 1:k) indicating the cluster to which each point is allocated.

centers: A matrix of cluster centers.

totss: The total sum of squares.

withinss: Vector of within-cluster sum of squares, one component per cluster.

tot.withinss: Total within-cluster sum of squares, i.e. sum(withinss).

betweenss: The between-cluster sum of squares, i.e. $totss-tot.withinss$.

size: The number of points in each cluster.

If we print the results we’ll see that our groupings resulted in 2 cluster sizes of 30 and 20. We see the cluster centers (means) for the two groups across the four variables (Murder, Assault, UrbanPop, Rape). We also get the cluster assignment for each observation (i.e. Alabama was assigned to cluster 2, Arkansas was assigned to cluster 1, etc.).

k2 ## K-means clustering with 2 clusters of sizes 20, 30 ## ## Cluster means: ##      Murder    Assault   UrbanPop       Rape ## 1  1.004934  1.0138274  0.1975853  0.8469650 ## 2 -0.669956 -0.6758849 -0.1317235 -0.5646433 ## ## Clustering vector: ##        Alabama         Alaska        Arizona       Arkansas     California ##              1              1              1              2              1 ##       Colorado    Connecticut       Delaware        Florida        Georgia ##              1              2              2              1              1 ##         Hawaii          Idaho       Illinois        Indiana           Iowa ##              2              2              1              2              2 ##         Kansas       Kentucky      Louisiana          Maine       Maryland ##              2              2              1              2              1 ##  Massachusetts       Michigan      Minnesota    Mississippi       Missouri ##              2              1              2              1              1 ##        Montana       Nebraska         Nevada  New Hampshire     New Jersey ##              2              2              1              2              2 ##     New Mexico       New York North Carolina   North Dakota           Ohio ##              1              1              1              2              2 ##       Oklahoma         Oregon   Pennsylvania   Rhode Island South Carolina ##              2              2              2              2              1 ##   South Dakota      Tennessee          Texas           Utah        Vermont ##              2              1              1              2              2 ##       Virginia     Washington  West Virginia      Wisconsin        Wyoming ##              2              2              2              2              2 ## ## Within cluster sum of squares by cluster: ## [1] 46.74796 56.11445 ##  (between_SS / total_SS =  47.5 %) ## ## Available components: ## ## [1] "cluster"      "centers"      "totss"        "withinss"     ## [5] "tot.withinss" "betweenss"    "size"         "iter"         ## [9] "ifault"

We can also view our results by using fviz_cluster. This provides a nice illustration of the clusters. If there are more than two dimensions (variables) fviz_cluster will perform principal component analysis (PCA) and plot the data points according to the first two principal components that explain the majority of the variance.

fviz_cluster(k2, data = df)

Alternatively, you can use standard pairwise scatter plots to illustrate the clusters compared to the original variables.

df %>%  as_tibble() %>%  mutate(cluster = k2$cluster,         state = row.names(USArrests)) %>%  ggplot(aes(UrbanPop, Murder, color = factor(cluster), label = state)) +  geom_text()

Because the number of clusters (k) must be set before we start the algorithm, it is often advantageous to use several different values of k and examine the differences in the results. We can execute the same process for 3, 4, and 5 clusters, and the results are shown in the figure:

k3 <- kmeans(df, centers = 3, nstart = 25) k4 <- kmeans(df, centers = 4, nstart = 25) k5 <- kmeans(df, centers = 5, nstart = 25) # plots to compare p1 <- fviz_cluster(k2, geom = "point", data = df) + ggtitle("k = 2") p2 <- fviz_cluster(k3, geom = "point",  data = df) + ggtitle("k = 3") p3 <- fviz_cluster(k4, geom = "point",  data = df) + ggtitle("k = 4") p4 <- fviz_cluster(k5, geom = "point",  data = df) + ggtitle("k = 5") library(gridExtra) grid.arrange(p1, p2, p3, p4, nrow = 2)

[exculis]

Woke up with the 'let's go get some tater totss' vine stuck in my head

[morenerdthanperson]

Emotional cocktails tag

Tagged by @nappyfreesince2003!

Match from the list below one emotion from each taste to each character in your WIP and describe why:

Sweet: Intelligence - Reason - Love - Hope - Confidence - Delight - Curiosity - Patience - Kindness - Courage - Peace - Humility - Awe Salty: Fear - Anger - Pride - Obsession - Envy - Regret - Shame - Guilt - Disappointment - Cynicism - Naivety - Emotion - Contempt Spicy: Dominance - Submission (just a funny little extra taste for those who prefer it ;) )

Ummm there are 37 named characters in my book so maybe not all of them?? I might just do this for my protagonist, Ahrin xD

Ahrin Latala

Sweet: Love. Ahrin is deeply connected to her adoptive family, and while it is incredibly difficult to gain her trust, once you do she will do anything for you (and by “anything”, I mean literally anything). The loss of a loved one is the inciting incident of her journey, and it is the need to keep the rest of her family safe, as well as the urge to protect those she doesn’t even know, that keeps her going.

Salty: Obsession. Being willing to do anything for those you love has the effect of making Ahrin set herself near-impossible tasks that are harmful to herself, and for her “letting go” is not an option, because in her mind it would mean “giving up”. 

Spicy: Dominant. Even in moments of vulnerability, Ahrin never lets her guard down. Nobody knows exactly what’s going through her head at any given time, and chances are that even when she’s having an important heart-to-heart with a loved one she’s still holding something back.

I’ll tag @gottaenjoythelittlethingzz and @shadowschild64 if you want to do this! <3

#tag game #TotSS #my wip

[wegnerrrichard]

EPL 20/21 - TOTS Prediction - FIFA 21 Squad - Futhead

Tots predictions premier league - FIFA 21 TOTS: Premier League Team of the Season - Predictions, Release date, Ratings, & more

The main event is nearly here on Ultimate Team.

The main event is nearly here on Ultimate Team

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Quite remarkable when you think about it. Ttos his three goals and two assists is not exactly groundbreaking he provides a constant threat down the right hand side while also providing exceptional defensive cover. He deserves all the plaudits he gets and at just 22 years of age he has a strong future ahead of him. That being said the Englishman has performed to a high standard.

With 14 goals and 4 assists to his name Rashford walks in on stats alone but he does deserve to be there, standing out in what is another mediocre season for United. There is tots predictions premier league lot of media hype surrounding him after several big performances and although his stats dont impress that totw with four goals and seven assists you have to pemier that he started the season in a defensive role. We have seen some great changes to promos this year too, so we are excited to see what EA has in store.

Lionel Messi will surely informative post the La Liga side, with former teammate Luis Suarez also set to feature after an excellent first season for Atletico Madrid. FIFA 08 Apr Team of the Season is not far away on FUT

[infinnite]

u totss cheat during testsss

yea s’why i faile my clases right? 

fuck you

#( asks ) #( anonymous ) #Anonymous

[thetauntinghydra]

[TotSS] The Shard Animus

Gift for a mod in the RP server I'm in and her OC Lasius!

──────⊹⊱✫⊰⊹────── Server Link (Discord) Lore Info: docs.google.com/document/d/1Yr…  ──────⊹⊱✫⊰⊹──────

#oc art #art #digital art #artists on tumblr #dragon art #wings of fire #wings of fire rp #dragon #hivewing #wingsoffire #wof