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Rational Breaks: a better way to work

I like the idea a lot.

However, I really need simple systems in my work routine. Things like "hitting a stopwatch, dividing by three, and carrying over previous rest time" already feels like it's a lot. Even though it's just a few seconds, I prefer if these systems take as little energy as possible to maintain.

What I thought was using a simple shell script: Just start it at the beginning of work, and hit a random key whenever I switch from work to rest or vice versa. It automatically keeps track of my break times.

I don't have Linux at home, but what I tried online ( https://www.onlinegdb.com/online_bash_shell ) is the following: (I am terrible at shell script so this is definitely not optimal, but I want to try something like this in the coming weeks. Perhaps one may want an additional warning or alarm sound if the break time gets below 0, but for me just "keeping track" is enough I think)

convertsecs() {
((h=${1}/3600))
((m=(${1}%3600)/60))
((s=${1}%60))
printf "%02d:%02d:%02d\n" $h $m $s
}

function flex_pomo() {
   current=0
   resttime=0
   total=0

   while true; do
   
       until read -s -n 1 -t 0.01; do 
           sleep 3
           current=$(( $current + 3 ))
           resttime=$(( $resttime + 1 ))
           total=$(( $total + 3 ))
           printf "\rCurrently working: Current interval: $(convertsecs $current), accumulated rest: $(convertsecs $resttime), total worktime: $(convertsecs $total)                           "
           done 
       printf "\nSwitching to break\n"
       current=0
       until read -s -n 1 -t 0.01; do 
           sleep 3
           current=$(( $current + 3 ))
           resttime=$(( $resttime - 3 ))
           printf "\rCurrently resting: Current interval: $(convertsecs $current), accumulated rest: $(convertsecs $resttime), total worktime: $(convertsecs $total)                           "
           done 
       printf "\nSwitching to work\n"
       current=0
   done
}

flex_pomo
 

The Variational Characterization of KL-Divergence, Error Catastrophes, and Generalization

Thanks, I finally got it. What I just now fully understood is that the final inequality holds with high  probability (i.e., as you say,  is the data), while the learning bound or loss reduction is given for 

The Variational Characterization of KL-Divergence, Error Catastrophes, and Generalization

Thanks, I was wondering what people referred to when mentioning PAC-Bayes bounds. I am still a bit confused. Could you explain how  and  depend on   (if they do) and how to interpret the final inequality in this light? Particularly I am wondering because the bound seems to be best when . Minor comment: I think ?

Squiggle: An Overview

The main thing that caught my attention was that random variables are often assumed to be independent. I am not sure if it is already included, but if one wants to allow for adding, multiplying, taking mixtures etc of random variables that are not independent, one way to do it is via copulas. For sampling based methods, working with copulas is a way of incorporating a moderate variety of possible dependence structures with little additional computational cost. 

The basic idea is to take a given dependence structure of some tractable multivariate random variable (e.g., one where we can produce samples quickly, like a multivariate Gaussian) and transfer its dependence structure to the individual one-dimensional distributions one likes to add, multiply, etc.