arXiv subjects

Karyn Morrissey

Publications and source records attributed to Karyn Morrissey.

11 recordsLinked to original sources

arXiv2026

MIRA: Mitigating Pole Geometry Bias in Recurrent Language Models

We show that the Transformer's recurrent successors --- such as selective state-space models (SSMs), Receptance Weighted Key Value (RWKV), and Retentive Network (RetNet) --- can be characterized as infinite-impulse-response (IIR) systems. The zero--pole geometries of these IIR systems characterize the temporal modes they can represent. However, to trade parallelism, the algebraic structures of the parameterizations of these recurrent systems often give rise to poles constrained to the real axis, thereby inducing an architectural bias in their recurrent dynamics. To unify these recurrent architectures and eliminate their pole geometry bias, inspired by the lattice--ladder realization of general IIR systems, we present \textsc{Mira} (\textbf{M}odal \textbf{I}IR \textbf{R}ecurrent \textbf{A}rchitecture), a unified family of linear-time recurrent architectures for building large language models (LLMs) with a highly parallel algebraic structure. Each \textsc{Mira} block realizes a higher-order IIR system that represents temporal modes from token streams through a lattice--ladder structure, followed by a feed-forward network (FFN). This realization enables the model to represent the temporal modes associated with both real and complex-conjugate poles. We pretrain \textsc{Mira-S}, a 12-layer small-scale LLM with 0.14B parameters composed entirely of \textsc{Mira} blocks. Throughout the pretraining, \textsc{Mira-S} achieves lower perplexity than strong recurrent baselines, exhibits stronger context-window generalization beyond the training length, and attains higher throughput with a dedicated CUDA kernel.

cs.AI↗

arXiv2026

Assessing flood-related wellbeing from public discourse in Ireland using large language model as judge, 2012--2025

Flooding is an environmental hazard that produces social and psychological consequences for exposed individuals, shaping threat appraisal, perceived coping capacity, access to social support, and confidence in institutional response. This paper studies flood-related wellbeing in Ireland from 2012 to 2025 using large-scale, unobtrusive public discourse from Meta Content Library. We develop a bespoke flood-wellbeing instrument organised with three domains: \emph{affective--cognitive distress appraisal}, \emph{diminished perceived efficacy and agency}, and \emph{perceived relational--institutional connectedness}. We then design a flood-wellbeing reasoning pipeline with large language models to extract multi-indicator, multi-domain flood-wellbeing measures from public discourse at scale. The analysis reveals both stressor activation and social--ecological resilience mediation pathways in Irish flood discourse. Flood stressors directly activate affective--cognitive distress appraisal, diminished perceived efficacy and agency, and perceived relational--institutional connectedness. The analysis further identifies two major mediation pathways: an appraisal--efficacy pathway linking flood exposure to threat appraisal, affective burden, uncertainty, and perceived coping capacity; and a social-buffering pathway in which social and institutional protective resources are associated with perceived efficacy and appraisal of flood-related adversity. The paper contributes an unobtrusive discourse-based approach to flood-wellbeing research by introducing a bespoke instrument, identifying activation and mediation pathways in flood discourse, and producing a temporal dataset for studying wellbeing, adaptation, and resilience under recurrent flood exposure.

cs.CY↗

arXiv2026

A population-level assessment framework for flood-related wellbeing from public discourse in Ireland

\ textbf{Background.} Ireland's position on the eastern North Atlantic exposes it to moisture-laden westerlies and frequent low-pressure systems, generating abundant precipitation and substantial pluvial and fluvial flood risk. Climate change is intensifying heavy rainfall and compound flood risk, with consequences extending beyond physical damage and conventional economic indicators. \ textbf{Method.} We present a framework for assessing flood-related wellbeing from unobtrusive public discourse over time. The framework develops an assessment instrument comprising three complementary constructs: \emph{distress}, \emph{functional disruption}, and \emph{institutional alienation}, capturing flood-related impacts on affective appraisal, daily functioning, and social and institutional connectedness. To enable population-scale analysis of complex cognitive and psychological responses, we develop a dedicated flood-related wellbeing reasoning model (\textsc{Wellbeing-Former}) that reads evidence of flood-related wellbeing, assigns evidence-grounded scores, and produces transparent rationales. \textbf{Data, Results \& Implications.} Using the research platform \textsc{MCL} (Meta Content Library), we construct a flood-related discourse dataset by querying posts from Ireland containing the terms \emph{flood}, \emph{rain}, and \emph{storm}. The resulting dataset comprises approximately \textbf{$224{,}000$} posts produced between 2012 and 2016. We apply the resulting \textsc{Wellbeing-Former} to this dataset to conduct a population-level analysis. This approach provides temporally sensitive evidence of experienced and anticipated flood impacts, complements survey-based assessment, and supports a multidimensional understanding of wellbeing under climate-related hazards for policy development, adaptation planning, public communication, and decision-making.

cs.CY↗

arXiv2026

A Stochastic--Geometric Theory of Scaling Laws in Grokking

Delayed generalization (\ie~grokking) refers to the phenomenon in which a neural network fits its training data early in training but only begins to generalize after a prolonged delay, often through an abrupt transition. Despite extensive empirical study, its underlying mechanism remains poorly understood. In this work, we first theoretically characterize a shell--core topological configuration of the reachable solution space induced by Adam's optimization dynamics with weight-shrinkage regularization, supported by empirical evidence. This optimization-induced topological configuration gives rise to grokking. In model's parameter space, random initialization solutions concentrate on a thin outer spherical shell, enclosing another spherical shell of memorization solutions, which in turn contains a core corresponding to the generalization solutions. Leveraging stopping-time theory, we then analyze the geometry of this topological configuration and the solution transition time at which optimization trajectories escape the memorization manifold and first reach the boundary of the generalization manifold. Our theoretical analysis derives grokking scaling laws for the learning rate, batch size, and $\ell_2$ regularization coefficient, which are further validated through experiments and shown to recover results from prior literature.

stat.ML↗

arXiv2026

Learning long term climate-resilient transport adaptation pathways under direct and indirect flood impacts using reinforcement learning

Climate change is expected to intensify rainfall and other hazards, increasing disruptions in urban transportation systems. Designing effective adaptation strategies is challenging due to the long-term, sequential nature of infrastructure investments, deep uncertainty, and complex cross-sector interactions. We propose a generic decision-support framework that couples an integrated assessment model (IAM) with reinforcement learning (RL) to learn adaptive, multi-decade investment pathways under uncertainty. The framework combines long-term climate projections (e.g., IPCC scenario pathways) with models that map projected extreme-weather drivers (e.g. rain) into hazard likelihoods (e.g. flooding), propagate hazards into urban infrastructure impacts (e.g. transport disruption), and value direct and indirect consequences for service performance and societal costs. Embedded in a reinforcement-learning loop, it learns adaptive climate adaptation policies that trade off investment and maintenance expenditures against avoided impacts. In collaboration with Copenhagen Municipality, we demonstrate the approach on pluvial flooding in the inner city for the horizon of 2024 to 2100. The learned strategies yield coordinated spatial-temporal pathways and improved robustness relative to conventional optimization baselines, namely inaction and random action, illustrating the framework's transferability to other hazards and cities.

cs.LG↗

arXiv2025

Incorporating Quality of Life in Climate Adaptation Planning via Reinforcement Learning

Urban flooding is expected to increase in frequency and severity as a consequence of climate change, causing wide-ranging impacts that include a decrease in urban Quality of Life (QoL). Meanwhile, policymakers must devise adaptation strategies that can cope with the uncertain nature of climate change and the complex and dynamic nature of urban flooding. Reinforcement Learning (RL) holds significant promise in tackling such complex, dynamic, and uncertain problems. Because of this, we use RL to identify which climate adaptation pathways lead to a higher QoL in the long term. We do this using an Integrated Assessment Model (IAM) which combines a rainfall projection model, a flood model, a transport accessibility model, and a quality of life index. Our preliminary results suggest that this approach can be used to learn optimal adaptation measures and it outperforms other realistic and real-world planning strategies. Our framework is publicly available: https://github.com/MLSM-at-DTU/maat_qol_framework.

cs.LG↗

arXiv2025

Climate Adaptation with Reinforcement Learning: Economic vs. Quality of Life Adaptation Pathways

Climate change will cause an increase in the frequency and severity of flood events, prompting the need for cohesive adaptation policymaking. Designing effective adaptation policies, however, depends on managing the uncertainty of long-term climate impacts. Meanwhile, such policies can feature important normative choices that are not always made explicit. We propose that Reinforcement Learning (RL) can be a useful tool to both identify adaptation pathways under uncertain conditions while it also allows for the explicit modelling (and consequent comparison) of different adaptation priorities (e.g. economic vs. wellbeing). We use an Integrated Assessment Model (IAM) to link together a rainfall and flood model, and compute the impacts of flooding in terms of quality of life (QoL), transportation, and infrastructure damage. Our results show that models prioritising QoL over economic impacts results in more adaptation spending as well as a more even distribution of spending over the study area, highlighting the extent to which such normative assumptions can alter adaptation policy. Our framework is publicly available: https://github.com/MLSM-at-DTU/maat_qol_framework.

cs.LG↗

arXiv2025

Using Reinforcement Learning to Integrate Subjective Wellbeing into Climate Adaptation Decision Making

Subjective wellbeing is a fundamental aspect of human life, influencing life expectancy and economic productivity, among others. Mobility plays a critical role in maintaining wellbeing, yet the increasing frequency and intensity of both nuisance and high-impact floods due to climate change are expected to significantly disrupt access to activities and destinations, thereby affecting overall wellbeing. Addressing climate adaptation presents a complex challenge for policymakers, who must select and implement policies from a broad set of options with varying effects while managing resource constraints and uncertain climate projections. In this work, we propose a multi-modular framework that uses reinforcement learning as a decision-support tool for climate adaptation in Copenhagen, Denmark. Our framework integrates four interconnected components: long-term rainfall projections, flood modeling, transport accessibility, and wellbeing modeling. This approach enables decision-makers to identify spatial and temporal policy interventions that help sustain or enhance subjective wellbeing over time. By modeling climate adaptation as an open-ended system, our framework provides a structured framework for exploring and evaluating adaptation policy pathways. In doing so, it supports policymakers to make informed decisions that maximize wellbeing in the long run.

cs.LG↗

arXiv2024

Climate Adaptation with Reinforcement Learning: Experiments with Flooding and Transportation in Copenhagen

Due to climate change the frequency and intensity of extreme rainfall events, which contribute to urban flooding, are expected to increase in many places. These floods can damage transport infrastructure and disrupt mobility, highlighting the need for cities to adapt to escalating risks. Reinforcement learning (RL) serves as a powerful tool for uncovering optimal adaptation strategies, determining how and where to deploy adaptation measures effectively, even under significant uncertainty. In this study, we leverage RL to identify the most effective timing and locations for implementing measures, aiming to reduce both direct and indirect impacts of flooding. Our framework integrates climate change projections of future rainfall events and floods, models city-wide motorized trips, and quantifies direct and indirect impacts on infrastructure and mobility. Preliminary results suggest that our RL-based approach can significantly enhance decision-making by prioritizing interventions in specific urban areas and identifying the optimal periods for their implementation. Our framework is publicly available: \url{https://github.com/MLSM-at-DTU/floods_transport_rl}.

cs.LG↗

arXiv2023

Green or greedy: the relationship between perceived benefits and homeowners' intention to adopt residential low-carbon technologies

Transitioning to a net-zero economy requires a nuanced understanding of homeowners decision-making pathways when considering the adoption of Low Carbon Technologies (LCTs). These LCTs present both personal and collective benefits, with positive perceptions critically influencing attitudes and intentions. Our study analyses the relationship between two primary benefits: the household-level financial gain and the broader environmental advantage. Focusing on the intention to adopt Rooftop Photovoltaic Systems, Energy Efficient Appliances, and Green Electricity Tariffs, we employ Partial Least Squares Structural Equation Modeling to demonstrate that the adoption intention of the LCTs is underpinned by the Theory of Planned Behaviour. Attitudes toward the LCTs are more strongly related to product-specific benefits than affective constructs. In terms of evaluative benefits, environmental benefits exhibit a higher positive association with attitude formation compared to financial benefits. However, this relationship switches as homeowners move through the decision process with the financial benefits of selected LCTs having a consistently higher association with adoption intention. At the same time, financial benefits also positively affect attitudes. Observing this trend across both low- and high-cost LCTs, we recommend that policymakers amplify homeowners' recognition of the individual benefits intrinsic to LCTs and enact measures that ensure these financial benefits.

econ.GN↗

arXiv2014

A Multinomial Model for Comorbidity in England of Longstanding CVD, Diabetes, and Obesity

From a public health perspective, previous research on comorbidity tends to have focused on identifying the most prevalent groupings of illnesses that demonstrate comorbidity, particularly among the elderly population, already in receipt of care. In contrast, little attention has been paid to possible socio-economic factors associated with increased rates of comorbidity or to the possibility of wider unrevealed need. Given the known relationship between CVD, diabetes and obesity and the strong socio-economic gradients in risk factors for each of the three diseases as single morbidities, this paper uses the Health Survey for England to examine the demographic and socio-economic determinants of each of the seven disease combinations in the English population. Using a multinomial logistic model, this research finds that gender is a significant predictor for all seven disease combinations. However, gender was not as influential as individual age or socio-economic profile. With regard to ethnicity, the black population presents a high obesity, diabetes and diabetes-related comorbidity risk, whilst the Asian population presents a high risk for diabetes and diabetes-related comorbidity but a low risk for obesity and comorbidity. Across the seven disease combinations, risk was lowest for those individuals with a high income (4 out of 7), in-work (4 out of 7), home owning (3 out of 7) and degree educated (3 out of 7). Finally, smokers have a lower risk rate of obesity (and related) than ex-smokers relative to individuals that never smoked (in all cases controlling for all other factors). The important influence of socioeconomic factors has implications for the spatial demand for services and the policy solutions adopted to tackle the increasing prevalence of comorbidity.

stat.AP↗