1. Last 7 days
    1. To-do Wir haben hier einen Fehleintrag von 20024 drinnen, der muss noch ausgebessert werden. 1937, 1939, 1980, 1982, 1985, 1986, 1987, 1990, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2002, 2003, 2004, 2005, 2006, 2007, 2012, 2013, 2017, 20024 Ist das eigentlich 2024?

      Dasselbe wie voher, 20024 checken

    2. df_clean$sector_type

      Ich sehe zwei Möglichkeiten das zu codieren:

      1. Nach Sektoren - Gibt es Unterschiede zwischen Manufacturing und anderen ? (Agriculture hat nur 6 obs..)
      2. Nach Arbeitsintensität - Macht es einen Unterschied ob ich einen Bürojob habe oder am Fließband/am Bau/am Acker arbeite?
    3. Transition to a larger size establishmentDummy (Current job: 0, New job (larger size establishment): 1, Nonemployment: 2)Employed, unemployed or inactive (individual)Dummy (0 or 1)14

      Anschauen um welche Effekte es hier genau geht

    4. Individual is unemployed, employed at a large firm or employed at a small firm at time tDummy (1: Individual is unemployed, 2 if he is employed at a large firm, and 3 if he is employed at a small firm at time t)Employed, unemployed or inactive (individual)Dummy (0 or 1)16Individual is unemployed, employed at a large firm or employed at a small firm at time t+2 (the last year in each panel)Dummy (1: Individual is unemployed, 2 if he is employed at a large firm, and 3 if he is employed at a small firm at time t + 2)Employed, unemployed or inactive (individual)Dummy (0 or 1), interaction20Job to non-employmentDummy (Current job: 0, New job: 1, Nonemployment: 2)Employed, unemployed or inactive (individual)Dummy (0 or 1)14

      Anschauen um welche Effekte es hier genau geht

    5. Differences and logEmployment (number of persons)Standard hours, log and differences

      Checken:

      1. Was sind logged differences?
      2. Wie liest man eine Regression der Form "logged differences" auf "logged differences", und kann man das vergleichbar machen?
  2. pressbooks.library.torontomu.ca pressbooks.library.torontomu.ca
    1. Janie said archly and fixed him back in bed. It was then she felt the pistol under the pillow. It gave her a quick ugly throb, but she didn’t ask him about it since he didn’t say.

      Foreshadowing

    2. Mrs. Turner’s brother was back on the muck and now he had this mysterious sickness. People didn’t just take sick like this for nothing.

      He doesn't even know he has rabies

    3. Tea Cake took it and filled his mouth then gagged horribly, disgorged that which was in his mouth and threw the glass upon the floor. Janie was frantic with alarm.

      RABIES SYMPTOMS

    4. He bought another rifle and a pistol and he and Janie bucked each other as to who was the best shot with Janie ranking him always with the rifle.

      They're loaded upp

  3. sellercentral.amazon.de sellercentral.amazon.de
    1. The major difference with Cycles is that Alice doesn’t simplypublish a transaction to send $10 to Bob; she first declares that she owes Bob $10

      I love how this shifts the perspective of money, into a trust base commitment to each other

    2. Perhaps Alice’s counterparty Bob doesn’t accept stablecoins;he only accepts ATOM. However, he may owe Carol $10, and Carol does accept the stablecoin. Byhaving Alice, Bob, and Carol all declare their intents, Cycles can transfer Alice’s stablecoin directlyto Carol (without them being aware of each other) and publish set-off notices for everyone

      I love the way how there is flexibility for all different kinds of forms of money and crypto!

    3. At regular intervals (e.g. daily, monthly),solvers execute and find solutions that clear the most obligations for the most people with the leastamount of liquidity, based on the published intents

      What is your experience around the most optimal interval? Is this a manual process for certain users in the platform or is this automated?

    4. Our defaultsolver is a min-cost max-flow algorithm called Multilateral Trade Credit Set-off (MTCS

      I am curious about the computational complexity of this algorithm. Especially when the userbase of the platform will scale up in the future. Did you already do any benchmark tests with larger userbase?

    5. our proposed payment system is designed without such intermediaries, and is focused onliquidity saving via set-off.

      Will Cycles also be connected (in the future) with existing clearinghouses or other clearing DeFi protocols?

    6. onnect their internal accounting system to a global network that optimizes theclearing of credits and debts using the available sources of liquidity

      Is there any information available on the onboarding process of SME's? As it seems to be a protocol heavy environment, I would state the importance of good UX abstraction. I am curious of the UX interface and the terminology used, so SMEs have a smooth onboarding process

      How feasible is it to onboard SMEs into a protocol-heavy environment like this? What UX abstractions will hide complexity of “obligations, tenders, and acceptances”?

    1. both groups had startedseventh grade with equivalent achievement test scores

      Starting at the same time or even later than the other person is not a problem with the right growth mindset.

    2. if youworked hard it meant that you didn’t have ability, andthat things would just come naturally to you if you did.

      Negative at first, dangerous belief that effort = failure sign.

      This explains why some students give up quickly when things get tough.

    3. we find that students with a fixed mindsetcare so much about how smart they will appear that theyoften reject learning opportunities

      Fixed mindset can block progress even when opportunities are helpful. Can we say the opposite? Because they deny too many opportunities to get better, they passively develop a fixed mindset from a young age.

    4. hey don’t necessarily believe that everyone has thesame abilities or that anyone can be as smart as Einstein,but they do believe that everyone can improve theirabilities. And they understand that even Einstein wasn’tEinstein until he put in years of focused hard work

      Not many people are born geniuses, but there are many geniuses who have succeeded through efforts.

    5. growth mindset were much more interested inlearning than in just looking smart in school.

      Growth mindset = value learning, not just appearance. I sometimes worry about looking smart too:(((

    6. As the students entered seventh grade, wemeasured their mindsets (along with a number of otherthings) and then we monitored their grades over thenext two years

      Longitudinal study = stronger evidence, not just a snapshot

    7. A fixed mindset makes chal-lenges threatening for students (because they believethat their fixed ability may not be up to the task

      Real examples, they believe that they can never do that with their poor brain, don't even try because of the fear. Overtime, this bad belief make their intelligence be fixed and this goes on and on until they change.

    8. studentswith this mindset worry about how much of this fixedintelligence they possess

      Some geniuses are born with natural talent, but not everyone is like that. The truth is that everyone is born with a certain level of intelligence, but it is not necessarily fixed.

    9. Internal and External Motivation

      This shows the article is part of a larger unit/theme about motivation. Will the article argue that growth mindset is a form of internal motivation?

    10. These different beliefs, or mindsets, cre-ate different psychological worlds

      That's important to believe and confident in yourself. Just a little bit change in mindsets, there can be change the whole worlds.

  4. pressbooks.library.torontomu.ca pressbooks.library.torontomu.ca
    1. Tea Cake, Ah don’t speck you seen his eyes lak Ah did. He didn’t aim tuh jus’ bite me, Tea Cake. He aimed tuh kill me stone dead. Ah’m never tuh fuhgit dem eyes. He wuzn’t nothin’ all over but pure hate. Wonder where he come from?”

      Rabies brooo

    2. Dey oughta know if it’s dangerous.

      The perception of white people was that they were highly educated and it was common to think that way

  5. ateliers-sp.gitpages.huma-num.fr ateliers-sp.gitpages.huma-num.fr