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   https://doi.org/10.1016/j.diin.2019.04.007  

   Casey, Peter ; Lindsay-Decusati, Rebecca ; Baggili, Ibrahim ; Breitinger, Frank ( July 2019 , Digital Investigation)
2. Ocean productivity from space: Commentary: Ocean Productivity From Space

   https://doi.org/10.1002/2016GB005582  

   Kahru, Mati ( January 2017 , Global Biogeochemical Cycles)
3. Using deep LSD to build operators in GANs latent space with meaning in real space

   https://doi.org/10.1371/journal.pone.0287736  

   Toledo-Marín, J. Quetzalcóatl ; Glazier, James A. ( June 2023 , PLOS ONE)

   Liu, Jin (Ed.)

   Generative models rely on the idea that data can be represented in terms of latent variables which are uncorrelated by definition. Lack of correlation among the latent variable support is important because it suggests that the latent-space manifold is simpler to understand and manipulate than the real-space representation. Many types of generative model are used in deep learning,e.g., variational autoencoders (VAEs) and generative adversarial networks (GANs). Based on the idea that the latent space behaves like a vector space Radford et al. (2015), we ask whether we can expand the latent space representation of our data elements in terms of an orthonormal basis set. Here we propose a method to build a set of linearly independent vectors in the latent space of a trained GAN, which we call quasi-eigenvectors. These quasi-eigenvectors have two key properties: i) They span the latent space, ii) A set of these quasi-eigenvectors map to each of the labeled features one-to-one. We show that in the case of the MNIST image data set, while the number of dimensions in latent space is large by design, 98% of the data in real space map to a sub-domain of latent space of dimensionality equal to the number of labels. We then show how the quasi-eigenvectors can be used for Latent Spectral Decomposition (LSD). We apply LSD to denoise MNIST images. Finally, using the quasi-eigenvectors, we construct rotation matrices in latent space which map to feature transformations in real space. Overall, from quasi-eigenvectors we gain insight regarding the latent space topology. 

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4. Heliophysics and space weather science at ∼1.5 AU: Knowledge gaps and need for space weather monitors at Mars

   https://doi.org/10.3389/fspas.2023.1064208  

   Lee, Christina O. ; Sánchez-Cano, Beatriz ; DiBraccio, Gina A. ; Mayyasi, Majd ; Xu, Shaosui ; Chamberlin, Phillip ; Davies, Emma ; Scolini, Camilla ; Filwett, Rachael J. ; Ramstad, Robin ; et al ( February 2023 , Frontiers in Astronomy and Space Sciences)

   This perspective article discusses the knowledge gaps and open questions regarding the solar and interplanetary drivers of space weather conditions experienced at Mars during active and quiescent solar periods, and the need for continuous, routine observations to address them. For both advancing science and as part of the strategic planning for human exploration at Mars by the late 2030s, now is the time to consider a network of upstream space weather monitors at Mars. Our main recommendations for the heliophysics community are the following: 1. Support the advancement for understanding heliophysics and space weather science at ∼1.5 AU and continue the support of planetary science payloads and missions that provide such measurements. 2. Prioritize an upstream Mars L1 monitor and/or areostationary orbiters for providing dedicated, continuous observations of solar activity and interplanetary conditions at ∼1.5 AU. 3. Establish new or support existing 1) joint efforts between federal agencies and their divisions and 2) international collaborations to carry out #1 and #2. 

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5. Micro-Space Complexity and Context in the Space-Time Variation in Enteric Disease Risk for Three Informal Settlements of Port au Prince, Haiti

   https://doi.org/10.3390/ijerph16050807  

   Curtis, Andrew ; Squires, Robert ; Rouzier, Vanessa ; Pape, Jean ; Ajayakumar, Jayakrishnan ; Bempah, Sandra ; Taifur Alam, Meer ; Alam, Md. ; Rashid, Mohammed ; Ali, Afsar ; et al ( March 2019 , International Journal of Environmental Research and Public Health)

   Diffusion of cholera and other diarrheal diseases in an informal settlement is a product of multiple behavioral, environmental and spatial risk factors. One of the most important components is the spatial interconnections among water points, drainage ditches, toilets and the intervening environment. This risk is also longitudinal and variable as water points fluctuate in relation to bacterial contamination. In this paper we consider part of this micro space complexity for three informal settlements in Port au Prince, Haiti. We expand on more typical epidemiological analysis of fecal coliforms at water points, drainage ditches and ocean sites by considering the importance of single point location fluctuation coupled with recording micro-space environmental conditions around each sample site. Results show that spatial variation in enteric disease risk occurs within neighborhoods, and that while certain trends are evident, the degree of individual site fluctuation should question the utility of both cross-sectional and more aggregate analysis. Various factors increase the counts of fecal coliform present, including the type of water point, how water was stored at that water point, and the proximity of the water point to local drainage. Some locations fluctuated considerably between being safe and unsafe on a monthly basis. Next steps to form a more comprehensive contextualized understanding of enteric disease risk in these environments should include the addition of behavioral factors and local insight. 

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