William (Bill) Peterson's contributions to ocean science, management, and policy

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Schwing, F. B., Sissenwine, M. J., Batchelder, H., Dam, H. G., Gomez-Gutierrez, J., Keister, J. E., Liu, H., & Peterson, J. O. William (Bill) Peterson's contributions to ocean science, management, and policy. Progress in Oceanography, 182, (2020): 102241, doi:10.1016/j.pocean.2019.102241.In addition to being an esteemed marine ecologist and oceanographer, William T. (Bill) Peterson was a dedicated public servant, a leader in the ocean science community, and a mentor to a generation of scientists. Bill recognized the importance of applied science and the need for integrated “big science” programs to advance our understanding of ecosystems and to guide their management. As the first US GLOBEC program manager, he was pivotal in transitioning the concept of understanding how climate change impacts marine ecosystems to an operational national research program. The scientific insight and knowledge generated by US GLOBEC informed and advanced the ecosystem-based management approaches now being implemented for fishery management in the US. Bill held significant leadership roles in numerous international efforts to understand global and regional ecological processes, and organized and chaired a number of influential scientific conferences and their proceedings. He was passionate about working with and training young researchers. Bill’s academic affiliations, notably at Stony Brook and Oregon State Universities, enabled him to advise, train, and mentor a host of students, post-doctoral researchers, and laboratory technicians. Under his collegial guidance they became critical independent thinkers and diligent investigators. His former students and colleagues carry on Bill Peterson’s legacy of research that helps us understand marine ecosystems and informs more effective resource stewardship and conservation

US GLOBEC: Program Goals, Approaches, and Advances

This special issue summarizes the major achievements of the US Global Ocean Ecosystem Dynamics (GLOBEC) program and celebrates its accomplishments. The articles grew out of a final symposium held in October 2009 under the auspices of the National Academy of Sciences Ocean Studies Board (http://usglobec.org/Symposium). This special issue updates the US GLOBEC "mid-life" Oceanography issue (Vol. 15, No. 2, 2002, http://tos.org/oceanography/archive/15-2.html), which put forward many of the goals and activities of the program, but was published while field work was still being conducted and results had yet to be synthesized across regional programs. The present special issue highlights the advances in understanding achieved through the synthesis of regional studies and pan-regional comparisons

Results from the Advance Power Technology Experiment on the Starshine 3 Satellite

The Starshine 3 satellite was put into orbit on September 30, 2001 as part of the Kodiak Star mission. Starshine 3’s primary mission is to measure the atmospheric density of the thermosphere and serve as a learning outreach tool for primary and secondary school age children. Starshine 3 also carries a power technology experiment. Starshine 3 has a small, 1 Watt power system using state-of-the-art components. Eight small clusters of solar cells are distributed across the surface. Each cluster consists of a 6-cell string of 2 cm x 2 cm, GaInP/GaAs/Ge, triple-junction solar cells. These cells have twice the power-to-area ratio as traditional silicon solar cells and 25% more power than GaAs cells. Starshine 3 also carries novel integrated microelectronic power supplies (IMPS). The idea behind an IMPS unit is to allow greater flexibility in circuit design with a power source not tied to a central bus. Each IPS is used to provide 50 microwatts of continuous power throughout the mission. Early results show that this design can be used to provide continuous power under very adverse operating conditions

Brief of Tax Law Professors as \u3ci\u3eAmici Curiae\u3c/i\u3e in Support of Petitioner in \u3ci\u3eLoudoun County, Virginia v. Dulles Duty Free, LLC\u3c/i\u3e

Amici are professors of tax law at universities across the United States. As scholars and teachers, they have considered the doctrinal roots and practical consequences of judicial limits on state and local taxation. Amici join this brief solely on their own behalf and not as representatives of their universities. A full list of amici appears in the Appendix to this brief

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG- bench). BIG-bench currently consists of 204 tasks, contributed by 450 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood develop- ment, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google- internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting