for - paper

paper - title: Carbon Consumption Patterns of Emerging Middle Class - year: 2020 - authors: Never et al.

summary - This is an important paper that shows the pathological and powerful impact of the consumer story to produce a continuous stream of consumers demanding a high carbon lifestyle - By defining success in terms of having more stuff and more luxurious stuff, it sets the class transition up for higher carbon consumption - The story is socially conditioned into every class, ensuring a constant stream of high carbon emitters. - It provides the motivation to - escape poverty into the lower middle class - escape the lower middle class into the middle class - escape the middle class into the middle-upper class - escape the middle-upper class into the upper class - With each transition, average carbon emissions rise - Unless we change this fundamental story that measures success by higher and higher levels of material consumption, along with their respectively higher carbon footprint, we will not be able to stay within planetary boundaries in any adequate measure - The famous Oxfam graphs that show that - 10% of the wealthiest citizens are responsible for 50% of all emissions - 1% of the wealthiest citizens are responsible for 16% of all emissions, equivalent to the bottom 66% of emissions - but it does not point out that the consumer story will continue to create this stratification distribution

from - search - google - research which classes aspire to a high carbon lifestyle? - https://www.google.com/search?q=research+which+classes+aspire+to+a+high+carbon+lifestyle%3F&oq=&gs_lcrp=EgZjaHJvbWUqCQgGECMYJxjqAjIJCAAQIxgnGOoCMgkIARAjGCcY6gIyCQgCECMYJxjqAjIJCAMQIxgnGOoCMgkIBBAjGCcY6gIyCQgFECMYJxjqAjIJCAYQIxgnGOoCMgkIBxAjGCcY6gLSAQk4OTE5ajBqMTWoAgiwAgE&sourceid=chrome&ie=UTF-8 - search results returned of salience - Carbon Consumption Patterns of Emerging Middle Classes- This discussion paper aims to help close this research gap by shedding light on the lifestyle choices of the emerging middle classes in three middle-income ... - https://www.idos-research.de/uploads/media/DP_13.2020.pdf

for - dream research

Summary - This presents a new theory of dreams that challenge Freud and Jung's interpretation of dreams. - It is intriguing, as it posits that the dream state is the default state of the brain. - it makes more sense to me.

source - google search - does dreaming allow cognitive during waking state to be possible?

• https://dreams.ucsc.edu/Library/domhoff_2010a.html

SoundStorm

We present SoundStorm, a model for efficient, non-autoregressive audio generation. SoundStorm receives as input the semantic tokens of AudioLM, and relies on bidirectional attention and confidence-based parallel decoding to generate the tokens of a neural audio codec. Compared to the autoregressive generation approach of AudioLM, our model produces audio of the same quality and with higher consistency in voice and acoustic conditions, while being two orders of magnitude faster. SoundStorm generates 30 seconds of audio in 0.5 seconds on a TPU-v4. We demonstrate the ability of our model to scale audio generation to longer sequences by synthesizing high-quality, natural dialogue segments, given a transcript annotated with speaker turns and a short prompt with the speakers' voices.

https://threadreaderapp.com/thread/1601640985858957312.html

Example of a literature review/research workflow using online repositories (like Google Scholar, Scopus, Clarivate, etc.), Zotero, Research Rabbit, and Obsidian.

Saire, Josimar. E. Chire., & Masuyama, A. (2021). How Japanese citizens faced the COVID-19 pandemic?: Exploration from twitter [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/64x7s

Nsoesie, E. O., Oladeji, O., Abah, A. S. A., & Ndeffo-Mbah, M. L. (2021). Forecasting influenza-like illness trends in Cameroon using Google Search Data. Scientific Reports, 11(1), 6713. https://doi.org/10.1038/s41598-021-85987-9

COVID-19 Social Science Tracker - Google Sheets

ReconfigBehSci en Twitter: “a google doc tracking all app development: https://t.co/vzmyWWF0qj” / Twitter. (n.d.). Twitter. Retrieved April 16, 2020, from https://twitter.com/SciBeh/status/1243171088256860160

How scientists can respond to COVID-19. (n.d.). Google Docs. Retrieved April 24, 2020, from https://docs.google.com/document/d/1DYoQH2YmrhAXs_DYwYMWKNaIXTC21zkR31RoCVmXeC4/edit?usp=embed_facebook