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1. Chapter 7: Education

   Etchemendy, John; Fattorini, Loredana; Gangas, Lili; Gil, Yolanda; Goins, Rachel; Hinton, Laura; Koshy, Sonia; Lundgren, Kirsten; Maslej, Nestor; Cruz_Novohatski, Lisa; et al (April 2025, The AI Index 2025 Annual Report)

   Maslej, Nestor; Fattorini, Loredana; Perrault, Raymond; Gil, Yolanda; Parli, Vanessa; Kariuki, Njenga; Capstick, Emily; Reuel, Anka; Brynjolfsson, Erik; Etchemendy, John (Ed.)

   AI has entered the public consciousness through generative AI’s impact on work—enhancing efficiency and automating tasks—but it has also driven innovation in education and personalized learning. Still, while AI promises benefits, it also poses risks—from hallucinating false outputs to reinforcing biases and diminishing critical thinking. With the AI education market expected to grow substantially, ethical concerns about the technology’s misuse—AI tools have already falsely accused marginalized students of cheating—are mounting, highlighting the need for responsible creation and deployment. Addressing these challenges requires both technical literacy and critical engagement with AI’s societal impact. Expanding AI expertise must begin in K–12 and higher education in order to ensure that students are prepared to be responsible users and developers. AI education cannot exist in isolation—it must align with broader computer science (CS) education efforts. This chapter examines the global state of AI and CS education, access disparities, and policies shaping AI’s role in learning. This chapter was a collaboration prepared by the Kapor Foundation, CSTA, PIT-UN and the AI Index. The Kapor Foundation works at the intersection of racial equity and technology to build equitable and inclusive computing education pathways, advance tech policies that mitigate harms and promote equitable opportunity, and deploy capital to support responsible, ethical, and equitable tech solutions. The CSTA is a global membership organization that unites, supports, and empowers educators to enhance the quality, accessibility, and inclusivity of computer science education. The Public Interest Technology University Network (PIT-UN) fosters collaboration between universities and colleges to build the PIT field and nurture a new generation of civic-minded technologists. 

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2. Identification of Receptor Binding Proteins in Flagellotropic Agrobacterium Phage 7-7-1

   https://doi.org/10.3390/v13071267  

   Gonzalez, Floricel; Scharf, Birgit E. (July 2021, Viruses)

   The rapid discovery of new and diverse bacteriophages has driven the innovation of approaches aimed at detailing interactions with their bacterial hosts. Previous studies on receptor binding proteins (RBPs) mainly relied on their identification in silico and are based on similarities to well-characterized systems. Thus, novel phage RBPs unlike those currently annotated in genomic and proteomic databases remain largely undiscovered. In this study, we employed a screen to identify RBPs in flagellotropic Agrobacterium phage 7-7-1. Flagellotropic phages utilize bacterial flagella as receptors. The screen identified three candidate RBPs, Gp4, Gp102, and Gp44. Homology modelling predicted that Gp4 is a trimeric, tail associated protein with a central β-barrel, while the structure and function of Gp102 and Gp44 are less obvious. Studies with purified Gp41-247 confirmed its ability to bind and interact with host cells, highlighting the robustness of the RBP screen. We also discovered that Gp41-247 inhibits the growth of host cells in a motility and lipopolysaccharide (LPS) dependent fashion. Hence, our results suggest interactions between Gp41-247, rotating flagellar filaments and host glycans to inhibit host cell growth, which presents an impactful and intriguing focus for future studies. 

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3. Halving ISO(7) supergravity

   https://doi.org/10.1007/JHEP11(2019)143  

   Guarino, Adolfo; Tarrío, Javier; Varela, Oscar (November 2019, Journal of High Energy Physics)

   null (Ed.)
4. CloudPatch-7 Hyperspectral Dataset

   https://doi.org/10.21227/fgb9-qs51  

   Yan, Hua Yan; Zheng, Rachel Zheng; Boehm, Brandon Boehm; Shaga, Sameer Shaga; Black, Derienne Black; Russell, Randy Russell; Kursun, Olcay Kursun (July 2024, IEEE DataPort)

   The CloudPatch-7 Hyperspectral Dataset comprises a manually curated collection of hyperspectral images, focused on pixel classification of atmospheric cloud classes. This labeled dataset features 380 patches, each a 50x50 pixel grid, derived from 28 larger, unlabeled parent images approximately 5000x1500 pixels in size. Captured using the Resonon PIKA XC2 camera, these images span 462 spectral bands from 400 to 1000 nm. Each patch is extracted from a parent image ensuring that its pixels fall within one of seven atmospheric conditions: Dense Dark Cumuliform Cloud, Dense Bright Cumuliform Cloud, Semi-transparent Cumuliform Cloud, Dense Cirroform Cloud, Semi-transparent Cirroform Cloud, Clear Sky - Low Aerosol Scattering (dark), and Clear Sky - Moderate to High Aerosol Scattering (bright). Incorporating contextual information from surrounding pixels enhances pixel classification into these 7 classes, making this dataset a valuable resource for spectral analysis, environmental monitoring, atmospheric science research, and testing machine learning applications that require contextual data. Parent images are very big in size, but they can be made available upon request. 

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5. Dielectron production in proton-proton collisions at s=7$$ \sqrt{s}=7 $$ TeV

   https://doi.org/10.1007/JHEP09(2018)064  

   Acharya, S.; Acosta, F. T.-.; Adamová, D.; Adolfsson, J.; Aggarwal, M. M.; Aglieri Rinella, G.; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahn, S. U.; et al (September 2018, Journal of High Energy Physics)