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1. What we talk about when we talk about colors

   https://doi.org/10.1073/pnas.2109237118  

   Twomey, Colin R.; Roberts, Gareth; Brainard, David H.; Plotkin, Joshua B. (September 2021, Proceedings of the National Academy of Sciences)

   Names for colors vary widely across languages, but color categories are remarkably consistent. Shared mechanisms of color perception help explain consistent partitions of visible light into discrete color vocabularies. But the mappings from colors to words are not identical across languages, which may reflect communicative needs—how often speakers must refer to objects of different color. Here we quantify the communicative needs of colors in 130 different languages by developing an inference algorithm for this problem. We find that communicative needs are not uniform: Some regions of color space exhibit 30-fold greater demand for communication than other regions. The regions of greatest demand correlate with the colors of salient objects, including ripe fruits in primate diets. Our analysis also reveals a hidden diversity in the communicative needs of colors across different languages, which is partly explained by differences in geographic location and the local biogeography of linguistic communities. Accounting for language-specific, nonuniform communicative needs improves predictions for how a language maps colors to words, and how these mappings vary across languages. Our account closes an important gap in the compression theory of color naming, while opening directions to study cross-cultural variation in the need to communicate different colors and its impact on the cultural evolution of color categories. 

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2. What Does Learning About Time Tell About Outdoor Scenes?

   https://doi.org/10.1109/AIPR57179.2022.10092235  

   Zhang, Zeyu; Baker, Callista; Azam-Naseeruddin, Noor; Shen, Jingzhou; Pless, Robert (October 2022, Proceedings of the 2022 IEEE Applied Imagery Pattern Recognition Workshop (AIPR))

   In this paper, we explore the potential of utilizing time-stamps as labels for Deep Learning from webcams, surveillance cameras, and other fixed viewpoint image situations. Specifically, we explore if learning to classify images by the time they were taken uncovers interesting patterns and behaviors in the scenes captured by these cameras. We describe approaches to building datasets with large quantities of images and their accompanying labels, making them suitable for large-scale deep learning approaches. We share our results from the initial deep learning experiments. 

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3. About Roy Glauber

   https://doi.org/10.1002/ntls.20220064  

   Friedrich, Bretislav; Kleppner, Daniel; Herschbach, Dudley (July 2023, Natural Sciences)

   Abstract We recount the life, work, and legacy of the theoretical physicist Roy Glauber (1925–2018). Admitted to Harvard at age 16, called upon to participate in the Manhattan Project at age eighteen, and appointed to the Harvard Physics faculty at age 29, Glauber is credited with seminal contributions to three separate fields of physics: nuclear scattering, statistical physics, and foundational work in quantum optics, which earned him the 2005 Nobel Prize in Physics. Over decades, Glauber was also a dedicated teacher of high‐school, college, and graduate students. His pedagogical gifts are reflected in his lucid papers that read as if they were written yesterday. Key pointsperspective on the life, work, and legacy of the 2005 Physics Nobel laureate Roy Glauber (1925–2018)impact of Glauber's contributions on large swaths of physics, from nuclear to optical to condensed matterGlauber's work in quantum optics has nurtured burgeoning areas of quantum science and engineering 

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4. It's about power

   https://doi.org/10.1145/3306617  

   Vakil, Sepehr; Higgs, Jennifer (February 2019, Communications of the ACM)
5. Reasoning about rationality

   https://doi.org/10.1016/j.geb.2017.03.006  

   Bjorndahl, A.; Halpern, J.Y.; Pass, R. (July 2017, Games and Economic Behavior)