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1. Non-euclidean virtual reality II: explorations of H²×E

   Hart, Vi; Hawksley, Andrea; Matsumoto, Elisabetta A; Segerman, Henry (July 2017, Bridges 2017 Conference Proceedings)

   We describe our initial explorations in simulating non-euclidean geometries in virtual reality. Our simulation of the product of two-dimensional hyperbolic space with one-dimensional euclidean space is available at h2xe.hypernom.com. 

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2. Aether: leveraging linear programming for optimal cloud computing in genomics

   https://doi.org/10.1093/bioinformatics/btx787  

   Luber, Jacob M.; Tierney, Braden T.; Cofer, Evan M.; Patel, Chirag J.; Kostic, Aleksandar D.; Berger, ed., Bonnie (December 2017, Bioinformatics)

   Abstract MotivationAcross biology, we are seeing rapid developments in scale of data production without a corresponding increase in data analysis capabilities. ResultsHere, we present Aether (http://aether.kosticlab.org), an intuitive, easy-to-use, cost-effective and scalable framework that uses linear programming to optimally bid on and deploy combinations of underutilized cloud computing resources. Our approach simultaneously minimizes the cost of data analysis and provides an easy transition from users’ existing HPC pipelines. Availability and implementationData utilized are available at https://pubs.broadinstitute.org/diabimmune and with EBI SRA accession ERP005989. Source code is available at (https://github.com/kosticlab/aether). Examples, documentation and a tutorial are available at http://aether.kosticlab.org. Supplementary informationSupplementary data are available at Bioinformatics online. 

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3. Causal (Progressive) Encoding over Binary Symmetric Channels with Noiseless Feedback

   https://doi.org/10.1109/ISIT45174.2021.9518287  

   Antonini, Amaael; Gimelshein, Rita; Wesel, Richard D. (July 2021, 2021 IEEE International Symposium on Information Theory (ISIT))

   Traditional communication systems transmit a codeword only after all message bits are available at the transmitter. This paper joins Guo & Kostina and Lalitha et al. in developing approaches for causal encoding, where the transmitter may begin transmitting codeword symbols as soon as the first message bit arrives. Building on the posterior matching encoders of Horstein, Shayevitz & Feder, and Naghshvar et al., this paper extends our computationally efficient systematic encoder to progressively encode using only the message bits that are causally available. Systematic codes work well with posterior matching on a channel with feedback, and they provide an immediate benefit when causal encoding is employed instead of traditional encoding. Our algorithm captures additional gains in the interesting region where the transmission rate μ is higher than the source rate λ at which message bits become available. In this region, we improve performance further through the transmission of additional, non- systematic symbols before a traditional encoder would have even begun transmission. 

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4. Learning Multiple Networks via Supervised Tensor Decomposition

   Hu, Jiaxin; Lee, Chanwoo; Wang, Miaoyan (December 2020, Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), Vancouver, Canada.)

   We consider the problem of tensor decomposition with multiple side information available as interactive features. Such problems are common in neuroimaging, network modeling, and spatial-temporal analysis. We develop a new family of exponential tensor decomposition models and establish the theoretical accuracy guarantees. An efficient alternating optimization algorithm is further developed. Unlike earlier methods, our proposal is able to handle a broad range of data types, including continuous, count, and binary observations. We apply the method to diffusion tensor imaging data from human connectome project and identify the key brain connectivity patterns associated with available features. Our method will help the practitioners efficiently analyze tensor datasets in various areas. Toward this end, all data and code are available at https://CRAN.R-project.org/ package=tensorregress. 

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5. Learning Multiple Networks via Supervised Tensor Decomposition

   Hu, Jiaxin; Lee, Chanwoo; Wang, Miaoyan (December 2020, NeurIPS 2023, Machine Learning and the Physical Sciences Workshop)

   We consider the problem of tensor decomposition with multiple side information available as interactive features. Such problems are common in neuroimaging, network modeling, and spatial-temporal analysis. We develop a new family of exponential tensor decomposition models and establish the theoretical accuracy guarantees. An efficient alternating optimization algorithm is further developed. Unlike earlier methods, our proposal is able to handle a broad range of data types, including continuous, count, and binary observations. We apply the method to diffusion tensor imaging data from human connectome project and identify the key brain connectivity patterns associated with available features. Our method will help the practitioners efficiently analyze tensor datasets in various areas. Toward this end, all data and code are available at https://CRAN.R-project.org/ package=tensorregress. 

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