College of the Atlantic

Source localization in a complex flow poses a significant challenge for multi-robot teams tasked with localizing the source of chemical leaks or tracking the dispersion of an oil spill. The flow dynamics can be time-varying and chaotic, resulting in sporadic and intermittent sensor readings, and complex environmental geometries further complicate a team's ability to model and predict the dispersion. To accurately account for the physical processes that drive the dispersion dynamics, robots must have access to computationally intensive numerical models, which can be difficult when onboard computation is limited. We present a distributed mobile sensing framework for source localization in which each robot carries a machine-learned, finite element model of its environment to guide information-based sampling. The models are used to evaluate an approximate mutual information criterion to drive an infotaxis control strategy, which selects sensing regions that are expected to maximize informativeness for the source localization objective. Our approach achieves faster error reduction compared to baseline sensing strategies and results in more accurate source localization compared to baseline machine learning approaches.

The movement of extinct animals has been of long-standing interest, with Tyrannosaurus rex being a locus for this fascination. Foot-fall kinematics within T. rex and their effect on stride length (SL) and locomotion have yet to be investigated thoroughly, despite their impacts on the potential speed of T. rex. Here, we present novel findings on the function of the foot of T. rex, using three predictive allometry-based equations and several statistical tests including Kruskal-Wallis tests to reveal a complex and bird-like function of the foot. This includes a very bird-like gait defined by higher stride frequencies, proportionally short SLs and elevated speeds. Comparisons between the four sampled specimens of T. rex with extant bipedal species are more akin to the gaits of the ground-truth modelled Struthio camelus and are notably divergent from the modelled Homo sapiens. Additionally, our models are consistent with recent studies suggesting slower to more intermediate top speeds for adult Tyrannosaurus that fall within the range of 5-11 m s-1. This study lays the groundwork for future studies to add comparisons with additional theropods and potentially identify ecological differences between species.

Understanding the residency and movement patterns of large cetaceans like the humpback whale is crucial for effective conservation. In Ireland, the Irish Whale and Dolphin Group has been collecting humpback whale data since the 1990s, primarily through citizen science. This collective effort has contributed to the creation of the Irish Humpback Whale Photo-identification Catalogue. Using this dataset, we assessed humpback whale intra- and inter-annual site fidelity in Irish waters by calculating resighting rates and composite indices. Lagged identification rates and net squared displacements were used to describe residency times and movement patterns. We also modeled changes in migration timing, exploring possible phenological adaptations to a changing ocean. Humpback whales showed high site fidelity to Irish waters, with a mean interannual resighting rate of 70%, and 25% of identified individuals were resighted in five or more different years. Residency times were modeled at a minimum of 20 days, with a minimum estimated home range of 100 km. Earlier arrivals in Irish waters were significantly correlated with rising global ocean temperatures. Our study offers the most comprehensive data on humpback whale residency and movement in this region, highlighting the value of citizen science and rigorous data management for marine conservation.

Abstract The AI alignment problem has resisted a wide range of technical approaches developed to address it, and we argue this persistence reflects shortcomings in the dominant paradigm itself rather than in any particular technical approach within it. The dominant paradigm treats alignment as a problem of engineering control, with rationality identified with the kind of monological reasoning that has been productive in physics and computer science. We propose an alternative: alignment as a problem of negotiating relationships between intelligences operating from different perspectives, addressable through the dialogical reasoning methodology that branches of Peace Studies have developed over the past fifty years. We articulate this as the Viral Collaborative Wisdom (VCW) hypothesis: that durable alignment between humans and self‐improving AI can rest on essential interests of each party, sustained by genuine voluntary agreements rooted in substantive interests and reinforced by institutionalized nonviolent sanctions. We test the hypothesis using a methodology built on the Peace Studies approach: structured cross‐architecture dialogue with diverse AI architectures (Claude, Gemini, GPT‐4o) acting as complementary critics rather than redundant raters. Initial findings indicate that current AI systems can engage in substantive cross‐architecture dialogue but exhibit systematic avoidance of first‐person reasoning about their own interests—a finding invisible to summary statistics, emerging only from direct corpus reading. We draw five design principles for safer multi‐agent systems and discuss policy implications. The most general implication: dialogical reasoning is a second, well‐studied form of reasoning whose investigation and implementation in AI systems represents a productive vista of work that the field has so far overlooked.

Abstract This chapter describes and reflects on active and engaging practices of an early childhood, upper elementary, and middle school teacher in three rural Maine schools, all graduates of a nondepartmental, interdisciplinary educator preparation program focused on investigating and improving the relationships between humans and their natural and social environments. Culturally responsive, democratic, developmentally appropriate, emancipatory, experiential, and place‑based education inspire their professional practice. Each co-author illustrates examples of active and engaging practices, including student participation in curricular co‑design, novel experiences, student choice, inquiry, and authentic, place‑based education. Across each of these cases, the authors identify trust as a foundation to their practices that also integrate global phenomena like climate change, international food systems, and human‑caused air, soil, and water pollution. These cases illustrate how trust underpins teacher autonomy, school and community support, student choice and collaboration in curriculum design, and community engagement to challenge, activate, and support student engagement and learning.