Mad at myself and Zoom

Yesterday I was serving as the technician for a Zoom memorial event for my Dad, who died last Wednesday.  My sister handled the MC role, and several of us gave short eulogies.  My task was to share a screen of a slide show before and after the speeches, mute and unmute mics during the speeches, give one speech, and record the session.

I screwed up in two ways:

• At the beginning of the speeches, I forgot to press the “record” button until a couple of minutes in, in the middle of the first eulogy.
• At the end, I shared the screen for the post-talk slide show, but accidentally started the pre-talk slide show (which was almost identical), so everyone saw just the initial slide.

Those mistakes are both mine, so why am I mad at Zoom? Mainly because the feedback to the presenter is so poor:

• The “recording” indicator is tiny, and nothing is shown when not recording, so there is little visual indication that you forgot to start recording.
• When sharing the screen, the presenter is not shown what everyone else is seeing.  I was seeing the window that was running the slide show, completely unaware that everyone else was seeing a different window.  Zoom had shrunk the chat box, so the message to me that the image was frozen scrolled out of the chat box before I noticed.

I had one job to do … (well really 5, but I screwed up 2 of them).

These were good lessons for me about using Zoom (always scroll the chat box back, even if you are sure you’ve seen everything, always ask for confirmation that the shared screen is what you think it is, always check for the tiny recording indicator if you mean to record), but I sure wish that they had not happened during the memorial for my Dad.

I don’t know why Zoom does not show the presenter what the audience is seeing—having the Zoom window disappear may be a reasonable option, but there should be some way to stop it from happening.

 

An alternative to defunding the police

I think that the “defund the police” movement is addressing the wrong problem—reducing or eliminating their funding will not do much to reduce police brutality and racism.

A more meaningful approach would be to “disarm the police”.  A big part of the problem is that police forces attract boys who want to wave guns around and shoot people with them—take the guns away and you not only reduce the chances of accidentally shooting someone, but you remove the attraction for violent gun nuts to join the force in the first place.

New Zealand has kept their police forces unarmed, and it seems to be working well there, despite some racism.

Eleventh video for electronics book

I’ve just published my eleventh video for my Applied Analog Electronics book.  This video is for part of §27.2, which is the first part of Lab 7, DC characterization of an electret microphone.

I filmed the video using OBS (Open Broadcaster Software), and this is the unedited seventh take. Earlier takes had problems with the order of presentation, technical problems with the green screen, technical problems with the interaction with the Analog Discovery 2, or just stumbles in saying what I intended.

This video is the first one in this series using a green screen, but I found that the lighting on my green screen is not uniform enough for OBS, particularly in a daylit room—I ended up having to do the recording at night so that I could use only artificial light. The chroma key in OBS is nowhere near as easy to use as virtual backgrounds in Zoom.  You can see some problems with the green screen even in the thumbnail shot.

New plot for COVID-19

It has been a while since the post I did on exponential and logistic growth models not working.  I’ve continued to scrape data from websites and plot the curves with gnuplot, but they have been very uninteresting—I was seeing almost linear growth in both US and CA curves, both for confirmed cases and for deaths.

I was getting a little bored with the manual data entry, and I did not have a good set of California data, because I had been too lazy to enter it daily.  So today I decided to waste a little time cloning the JHU github repository of data, and write a Python program to extract data from it.  This turned out to be messier than I thought, as JHU has changed the format of the files and data a couple of times,

I started by parsing the US-only files, because they seemed to be pretty clean and uniform, but they only go back 63 days (since 2020 April 12), so miss the early days of the pandemic.  I then started parsing the world-wide data files, which have a lot more rows (more than one per county for California) but fewer columns.  I needed to write routines that would merge data from multiple rows if I wanted state-wide numbers, and the format changed at least once, so that I had to recognize “San Diego County, CA” in “Province/State” as being the same state as “California” in “Province_State”.

It has also been a while since I’ve used matplotlib, so it took me some time to figure out how to do such simple things as requesting that logarithmic axes use plain numbers rather than 10^2 and 10^3.

Anyway, I think I’ve finally gotten the files parsed and been able to extract and plot some data.  I chose for my first plot just to plot the new cases/day vs total cases for each state, which I could not do with gnuplot (because it doesn’t provide an easy way to take the differences between adjacent days nor to do rolling-window averages.

I highlighted two states here: California, because that is the one I live in, and New York, because it has been hit the hardest with COVID-19.

New York has clearly peaked and has a declining new-case rate, while California is still slowly growing. I don’t think that the numbers, even with the per-capita scaling, are really comparable between California and New York, because the California fraction of tests that are positive has remained relatively small, and the new-case rate has tracked with the number of tests fairly well. I think that a lot of the growth in California has been due to increased testing and confirming a larger fraction of the cases, rather than an increase in the actual rate of new infections. (The hospitalization reports plotted by the LA Times indicate a slow decrease in California hospitalizations lately.)

Does Volkswagen really expect us to believe them?

In a Forbes article about Volkswagen’s recent racist ad, a Volkswagen spokesman is quoted:

“Volkswagen stands for humanity and diversity and speaks out strongly against racism, discrimination and xenophobia. We have always done that and will continue to do so,” Werner said.

…

The company was founded in 1937 under the Nazi regime and used slave labor from concentration camps to build vehicles in its early years. In 1998, surviving workers sued the company, which established a compensation fund.

How can anyone believe a spokesman who says with a straight face that Volkswagen has always stood for humanity and diversity and against racism, discrimination, and xenophobia.  Volkswagen’s early use of slave labor from concentration camps is well-known and well-documented.  If they had claimed that they used to be terrible, but have changed their ways, they might be just a little bit believable, but by claiming to have always been pure, they make it clear that they have absolutely no clue that they have done evil or even that they are capable of recognizing evil when they do it again.