Eocene Terrestrial Mammals From Central Georgia

Descriptions of fossils of Eocene terrestrial mammals from the southeastern United States are rare, and particularly so in the Eocene sediments of Georgia. Here we describe a small collection of fossilized teeth and tooth fragments representing four mammalian taxa. The fossils were recovered by surface collecting overburden sediments and screen washing in situ Clinchfield Formation sediments exposed in an inactive kaolin mine, Hardie Mine, in Wilkinson County, Georgia. The Clinchfield Formation has been described as a Late Eocene coastal unit with abundant gastropods, bivalves, sharks, and rays. This is the first detailed description of terrestrial mammals from this unit. Although limited in diversity, this collection represents the most diverse Eocene-aged mammalian fauna described for the state

Plausible responses to the threat of rapid sea-level rise for the Thames Estuary

This paper considers the perceptions and responses of selected stakeholders to a scenarion of rapid rise in sea-level due to the collapse of the West Antarctic ice sheet, which could produce a global rise in sea-level of 5 to 6 metres. Through a process of dialogue involving one-to one interviews and a one-day policy exercise, we addressed influences on decision-making when information is uncertain and our ability to plan, prepare for and implement effective ways of coping with this extreme scenario. Through these interactions we hoped to uncover plausible responses to the scenario and identify potential weaknesses in our current flood management approaches to dealing with such an occurrence. By undertaking this exploratory exercise we hoped to find out whether this was a feasible way to deal with such a low probability but high consequence scenario. It was the process of finding a solution that interested us rather than the technical merits of one solution over another. We were not intending to produce definitive set of recommendations on how to respond but to gain insights into the process of making a decision, specifically what influences it and what assumptions are made.Sea level rise, London

Replay-Guided Adversarial Environment Design

Deep reinforcement learning (RL) agents may successfully generalize to new settings if trained on an appropriately diverse set of environment and task configurations. Unsupervised Environment Design (UED) is a promising self-supervised RL paradigm, wherein the free parameters of an underspecified environment are automatically adapted during training to the agent's capabilities, leading to the emergence of diverse training environments. Here, we cast Prioritized Level Replay (PLR), an empirically successful but theoretically unmotivated method that selectively samples randomly-generated training levels, as UED. We argue that by curating completely random levels, PLR, too, can generate novel and complex levels for effective training. This insight reveals a natural class of UED methods we call Dual Curriculum Design (DCD). Crucially, DCD includes both PLR and a popular UED algorithm, PAIRED, as special cases and inherits similar theoretical guarantees. This connection allows us to develop novel theory for PLR, providing a version with a robustness guarantee at Nash equilibria. Furthermore, our theory suggests a highly counterintuitive improvement to PLR: by stopping the agent from updating its policy on uncurated levels (training on less data), we can improve the convergence to Nash equilibria. Indeed, our experiments confirm that our new method, PLR ⊥ , obtains better results on a suite of out-of-distribution, zero-shot transfer tasks, in addition to demonstrating that PLR ⊥ improves the performance of PAIRED, from which it inherited its theoretical framework

Ultraviolet and Optical Observations of OB Associations and Field Stars in the Southwest Region of the Large Magellanic Cloud

Using photometry from the Ultraviolet Imaging Telescope (UIT) and photometry and spectroscopy from three ground-based optical datasets we have analyzed the stellar content of OB associations and field areas in and around the regions N 79, N 81, N 83, and N 94 in the LMC. We compare data for the OB association Lucke-Hodge 2 (LH 2) to determine how strongly the initial mass function (IMF) may depend on different photometric reductions and calibrations. We also correct for the background contribution of field stars, showing the importance of correcting for field star contamination in determinations of the IMF of star formation regions. It is possible that even in the case of an universal IMF, the variability of the density of background stars could be the dominant factor creating the differences between calculated IMFs for OB associations. We have also combined the UIT data with the Magellanic Cloud Photometric Survey to study the distribution of the candidate O-type stars in the field. We find a significant fraction, roughly half, of the candidate O-type stars are found in field regions, far from any obvious OB associations. These stars are greater than 2 arcmin (30 pc) from the boundaries of existing OB associations in the region, which is a distance greater than most O-type stars with typical dispersion velocities will travel in their lifetimes. The origin of these massive field stars (either as runaways, members of low-density star-forming regions, or examples of isolated massive star formation) will have to be determined by further observations and analysis.Comment: 16 pages, 10 figures (19 PostScript files), tabular data + header file for Table 1 (2 ASCII files). File format is LaTeX/AASTeX v.502 using the emulateapj5 preprint style (included). Also available at http://www.boulder.swri.edu/~joel/papers.html . To appear in the February 2001 issue of the Astronomical Journa

Evolving Curricula with Regret-Based Environment Design

Training generally-capable agents with reinforcement learning (RL) remains a significant challenge. A promising avenue for improving the robustness of RL agents is through the use of curricula. One such class of methods frames environment design as a game between a student and a teacher, using regret-based objectives to produce environment instantiations (or levels) at the frontier of the student agent's capabilities. These methods benefit from theoretical robustness guarantees at equilibrium, yet they often struggle to find effective levels in challenging design spaces in practice. By contrast, evolutionary approaches incrementally alter environment complexity, resulting in potentially open-ended learning, but often rely on domain-specific heuristics and vast amounts of computational resources. This work proposes harnessing the power of evolution in a principled, regret-based curriculum. Our approach, which we call Adversarially Compounding Complexity by Editing Levels (ACCEL), seeks to constantly produce levels at the frontier of an agent's capabilities, resulting in curricula that start simple but become increasingly complex. ACCEL maintains the theoretical benefits of prior regret-based methods, while providing significant empirical gains in a diverse set of environments. An interactive version of this paper is available at https://accelagent.github.io

Grounding Aleatoric Uncertainty for Unsupervised Environment Design

Adaptive curricula in reinforcement learning (RL) have proven effective for producing policies robust to discrepancies between the train and test environment. Recently, the Unsupervised Environment Design (UED) framework generalized RL curricula to generating sequences of entire environments, leading to new methods with robust minimax regret properties. Problematically, in partially-observable or stochastic settings, optimal policies may depend on the ground-truth distribution over aleatoric parameters of the environment in the intended deployment setting, while curriculum learning necessarily shifts the training distribution. We formalize this phenomenon as curriculum-induced covariate shift (CICS), and describe how its occurrence in aleatoric parameters can lead to suboptimal policies. Directly sampling these parameters from the ground-truth distribution avoids the issue, but thwarts curriculum learning. We propose SAMPLR, a minimax regret UED method that optimizes the ground-truth utility function, even when the underlying training data is biased due to CICS. We prove, and validate on challenging domains, that our approach preserves optimality under the ground-truth distribution, while promoting robustness across the full range of environment settings