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https://preview.redd.it/qweog80ryc681.jpg?width=1280&format=pjpg&auto=webp&s=9fb91f0f047d8dbe120dc65feb0e18ab43e57c38
[Biweekly Quest Distribution for a Brawl Pass Season]
Wk1
500x1, 250x2; 250x1 = 1.25k tokens
250x3; 250x1 = 1k; 2.25k
Wk2
250x3; 250x1 = 1k
500x1, 250x2; 250x1 = 1.25k; 2.25k, 4.5k
Wk3
500x1, 250x2; 250x1 = 1.25k
500x1, 250x2; 250x1 = 1.25k; 2.5k, 7k
Wk4
500x1, 250x2; 250x1 = 1.25k
500x1, 250x2; 250x1 = 1.25k; 2.5k, 9.5k
Wk5
500x1, 250x2; 250x1 = 1.25k
500x2, 250x1; 250x1 = 1.5k; 2.75k, 12.25k
Wk6
500x1, 250x2; 250x1 = 1.25k
500x2, 250x1; 250x1 = 1.5k; 2.75k, 15k
Wk7
500x2, 250x1; 250x1 = 1.5k
500x2, 250x1; 250x1 = 1.5k; 3k, 18k
Wk8
500x2, 250x1; 250x1 = 1.5k
500x2, 250x1; 250x1 = 1.5k; 3k, 21k
Wk9
500x1, 250x2; 250x1 = 1.25k
500x2, 250x1; 250x1 = 1.5k; 2.75k, 23.75k
Wk10
500x1, 250x2; 250x1 = 1.25k
500x1, 250x2; 250x1 = 1.25k, 2.5k, 26.25k
[Dailies]
Daily Tokens Β Β Β Β Β Β Β Β Β Β 200
Daily QuestΒ Β Β Β 100x2 = 200
Daily Click Β Β Β Β Β Β Β 5x6 = 30
Daily Map Maker 10x3 = 30
= 460 tokens daily
[Weeklies]
Sa Weekly Quest Β Β 100x5 = 500Β
Sa Weekly ClickΒ Β Β Β Β Β 50 = 50
= 550 tokens weekly
[Weekly Total]
Dailies 460x7 = 3220
Weeklies 550x1 = 550
= 3770 weekly if get all rewards excluding biweekly quests
[Conclusions from the data]
For those who buy the Brawl Pass:
- Fastest time to get to Tier 30 is 1 week & 6 days into the season*
- Fastest time to get to Tier 70 is 5 weeks & 3 days into the season*
- Minimum to get to Tier 70 is the last 2 weeks of the season & all the season's biweekly quests*
- Maximum 60.9 big boxes after Tier 70 at the end of the season (subtract 10 if didn't buy the pass, subtract another 2 if you didn't get the season's chromatic brawler)*
*Only sources of tokens factored in: Dailies, Weeklies, Biweekly Quests, New Chromatic Brawler Quest (1k tokens)
{So no doublers, double token weekends, brawler rank ups, etc}
For a toy example, suppose there are 1-to-5 labels to which a document could be assigned: [sports, politics, science, music, art]. The labels are not necessarily balanced; maybe 30% are sports, then 25%, 20%, 15%, and 10% for the remaining 4. Further, some labels tend to co-occur, like science and politics, or art and music.
For the training-validation sample split, as an experiment, single-labeled bills (80% of the sample) are placed into training, and multi-labeled bills are validation. I basically want to test whether a model that is technically equipped for multilabel classification (in terms of the output layer & its activation function) can detect when something is multi-label even after not seeing it before. I know there's probably some zero-shot learning approach for handling this, but I am specifically interested in classic supervised learning.
What I find is that the model's precision is very high, i.e. when it claims a label is present, it is seldom wrong. However, the model's recall is very low, i.e. it fails to recover many labels that are actually present. The total number of labels predicted for the validation set is much lower than the ground-truth, and many of these are predicted as being single-label as a result.
I'm hoping to basically prepare the model for a shift in label distribution, by saying "look, any combination of labels is technically possible, whether you've seen it in training or not." It seems to be quite reliant on these distributional clues, i.e. if it predicts "science" it will automatically add more weight to the probability of "politics." Is there a way to prevent it from doing that? Sorry if the question isn't clear.
I am a staker and (new) liquidity provider and could not find any information about the distribution of occx per occ staked and what not. Thanks!
I was inspired by the recent Darkhorse Podcast livestream interview with Dr. Robert Malone and Steve Kirsh to seek data from the Pfizer organ distribution study in rats. The graph shared on the podcast and Trialsitenews contains accurate data, but leaves out a few important organ systems and does not make clear these distributions were studies in rats. I've made a graph containing the full organ distribution dataset. I'd be very interested in hearing your thoughts.
Original report: https://www.pmda.go.jp/drugs/2021/P20210212001/672212000_30300AMX00231_I100_1.pdf.Data in Graph: P.6 and P.7, "PHARMACOKINETICS: ORGAN DISTRIBUTION CONTINUED"
Thoughts on vaccines:
- The COVID spike protein is toxic/bioactive by itself, perhaps more than other coronaviruses (data?).-- Source: Salk Institute, https://www.salk.edu/news-release/the-novel-coronavirus-spike-protein-plays-additional-key-role-in-illness/
- All current vaccines (both mRNA and viral vector types) code for spike protein production in cells.-- Source: https://theconversation.com/covid-vaccines-focus-on-the-spike-protein-but-heres-another-target-150315
- mRNA vaccines were assumed to deliver lipid nanoparticles only into the shoulder muscle injection site and local lymphatic system, leading to developing immunity with lower risk than contracting COVID and having the spike protein replicate throughout your body.-- Source: https://factcheck.afp.com/posts-misrepresent-us-study-dangers-coronavirus-spike-protein"Annette Beck-Sickinger, professor of biochemistry at Leipzig University, said spike proteins are only created on the surface of the muscle cells when a person is vaccin
Hi guys,
I learned college statistics but only at the undergraduate level. I feel like it's hard to apply what I learned to real-life situations, unlike self-contained problem sets.
Here's what I'm trying to do:
I want to estimate the population's distribution with some pieces of information I gathered that are not sufficient to estimate the distribution perfectly.
I took a test and I have some information like:
- There were 59 students. (n=59)
- Mean is 75
- Top score was 96
- The lowest was 48
- There are 13 students within 60 <= x < 75 range
- There are 3 students under 60 (x < 60)
If I assume a normal distribution for the population, I already have the mean so now have to know the standard deviation of the population to get the distribution.
However, instead of standard deviation, I got these bunch of partial information that seem useful but don't know how to actually use them to calculate the population distribution.
Is there a standardized way to update my knowledge of the population based on this little partial information? I feel like this should be related to Bayesian update but this seems totally different from what I read in the textbook.
Cryptolaxy
@Cryptolaxy
TOKEN SUPPLY ANALYSIS OF THE PJTs WITH MARKET CAP FROM $120M TO $200M AND FINITE MAX SUPPLY The infographic provides information about the distribution of tokens out of the maximum token supply. $RLY $BAL $BNX $TLM $DODO $JST $ERN $CHR $FORTH $STPT $ALPACA $AVA $MFT
https://preview.redd.it/i6jsj14lumu71.png?width=900&format=png&auto=webp&s=6cd582e5037d894794fa2d6c0c3a6d238b0bb8be
Hi guys,
I learned college statistics but only at the undergraduate level. I feel like it's hard to apply what I learned to real-life situations, unlike self-contained problem sets.
Here's what I'm trying to do:
I want to estimate the population's distribution with some pieces of information I gathered that are not sufficient to estimate the distribution perfectly.
I took a test and I have some information like:
- There were 59 students. (n=59)
- Mean is 75
- Top score was 96
- The lowest was 48
- There are 13 students within 60 <= x < 75 range
- There are 3 students under 60 (x < 60)
If I assume a normal distribution for the population, I already have the mean so now have to know the standard deviation of the population to get the distribution.
However, instead of standard deviation, I got these bunch of partial information that seem useful but don't know how to actually use them to calculate the population distribution.
Is there a standardized way to update my knowledge of the population based on this little partial information? I feel like this should be related to Bayesian update but this seems totally different from what I read in the textbook.
