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1. The Vector Heat Method

   https://doi.org/10.1145/3243651  

   Sharp, Nicholas; Soliman, Yousuf; Crane, Keenan (June 2019, ACM Transactions on Graphics)
2. The Vector Data Ecosystem

   https://doi.org/10.7274/27854508  

   Ryan, Sadie J; Johnson, Leah R; Lippi, Catherine A; Cator, Lauren; Pawar, Samraat; Rund, Samuel_S C (January 2023, University of Notre Dame)

   A growing body of information on vector-borne diseases has arisen as increasing research focus has been directed towards the need for anticipating risk, optimizing surveillance, and understanding the fundamental biology of vector-borne diseases to direct efforts to control and mitigation. The scope and scale of this information, in the form of data, comprising database efforts, data storage, and serving approaches, mean that it is distributed across many formats and data types. Data ranges from collections records to molecular characterization, geospatial data to interactions of vectors and traits, infection experiments to field trials. New initiatives arise, often spanning the effort traditionally siloed in specific research disciplines, and other efforts wane, perhaps in response to funding declines, different research directions, or lack of sustained interest. Thusly, the world of vector data - the Vector Data Ecosystem - can become unclear in scope, and the flows of data through these various efforts can become stymied by obsolescence, or simply by gaps in access and interoperability. As increasing attention is paid to creating FAIR (Findable Accessible Interoperable, and Reusable) data, simply characterizing what is ‘out there’, and how these existing data aggregation and collection efforts interact, or interoperate with each other, is a useful exercise. This website and related project presents a list of vector data curation efforts, a brief description of their stated scope and purpose, and level of accessibility. The Vector Data Ecosystem by the University of Notre Dame Center for Research Computing, and is being developed and maintained as part of the NSF funded VectorByte Initiative (www.vectorbyte.org). 

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3. The long-distance behaviour of the vector-vector correlator from pion-pion scattering

   https://doi.org/10.22323/1.430.0073  

   Paul, Srijit; Hanlon, Andrew D.; Hoerz, Ben; Mohler, Daniel; Morningstar, Colin; Wittig, Hartmut (April 2023, 39th International Symposium on Lattice Field Theory)

   We present the estimation of the long distance behaviour of the vector-vector correlator computed on a lattice QCD ensemble generated with 2 + 1 flavour physical Wilson clover quarks. The long distance regime of the correlator is dominated by multi-hadronic scattering states. We reconstruct the correlator in this regime using the pion-pion scattering states in the 𝐼 = 1 channel. The vector-vector correlator appears in the integrand to estimate the hadronic vacuum polarization contributions to the anomalous magnetic moment of the muon. Therefore, an improved estimation of the correlator will help resolve the tension between (𝑔 − 2)𝜇 experiment and theory. 

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4. Characterizing the Vector Data Ecosystem

   https://doi.org/10.1093/jme/tjad009  

   Lippi, Catherine A; Rund, Samuel S; Ryan, Sadie J (February 2023, Journal of Medical Entomology)

   Faraji, Ary (Ed.)

   Abstract A growing body of information on vector-borne diseases has arisen as increasing research focus has been directed towards the need for anticipating risk, optimizing surveillance, and understanding the fundamental biology of vector-borne diseases to direct control and mitigation efforts. The scope and scale of this information, in the form of data, comprising database efforts, data storage, and serving approaches, means that it is distributed across many formats and data types. Data ranges from collections records to molecular characterization, geospatial data to interactions of vectors and traits, infection experiments to field trials. New initiatives arise, often spanning the effort traditionally siloed in specific research disciplines, and other efforts wane, perhaps in response to funding declines, different research directions, or lack of sustained interest. Thusly, the world of vector data – the Vector Data Ecosystem – can become unclear in scope, and the flows of data through these various efforts can become stymied by obsolescence, or simply by gaps in access and interoperability. As increasing attention is paid to creating FAIR (Findable Accessible Interoperable, and Reusable) data, simply characterizing what is ‘out there’, and how these existing data aggregation and collection efforts interact, or interoperate with each other, is a useful exercise. This study presents a snapshot of current vector data efforts, reporting on level of accessibility, and commenting on interoperability using an illustration to track a specimen through the data ecosystem to understand where it occurs for the database efforts anticipated to describe it (or parts of its extended specimen data). 

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5. Improving the regularity of vector fields

   https://doi.org/10.1016/j.jfa.2022.109537  

   Street, Brian; Yao, Liding (September 2022, Journal of Functional Analysis)