Quantile regression is a powerful tool for detecting exposure-outcome associations given covariates across different parts of the outcome's distribution, but has two major limitations when the aim is to infer the effect of an exposure. Firstly, the exposure coefficient estimator may not converge to a meaningful quantity when the model is misspecified, and secondly, variable selection methods may induce bias and excess uncertainty, rendering inferences biased and overly optimistic. In this paper, we address these issues via partially linear quantile regression models which parametrize the conditional association of interest, but do not restrict the association with other covariates in the model. We propose consistent estimators for the unknown model parameter by mapping it onto a nonparametric main effect estimand that captures the (conditional) association of interest even when the quantile model is misspecified. This estimand is estimated using the efficient influence function under the nonparametric model, allowing for the incorporation of data-adaptive procedures such as variable selection and machine learning. Our approach provides a flexible and reliable method for detecting associations that is robust to model misspecification and excess uncertainty induced by variable selection methods. The proposal is illustrated using simulation studies and data on annual health care costs associated with excess body weight.
The rapid growth of anthropogenic debris in Earth’s orbit demands advanced robotic solutions for autonomous on-orbit capture and removal. In microgravity, uncontrolled debris exhibits complex six-degree-of-freedom (6-DoF) translational and rotational dynamics, making precise pre-positioning of robotic manipulators before grasping highly challenging. To address this, we present a reinforcement learning (RL) framework that employs a curriculum-based training strategy to progressively strengthen the agent’s 6-DoF pre-capture maneuvering capabilities. Using Soft Actor-Critic algorithm trained entirely in a PyBullet simulation, we incorporate domain randomization to enable effective zero-shot sim-to-real transfer, eliminating the need for additional real-world fine-tuning. The resulting policy demonstrates zero-shot deployment in a controlled Zero-G Lab setting, guiding a physical robotic gripper to adaptively synchronize its pose with a dynamically rotating target while avoiding collisions under conservative laboratory conditions. These results provide an experimental foundation for future autonomous robotic capture of tumbling space debris. Demonstration videos, technical details, and experimental results are available at baha2r.github.io/luxe24.
This article examines a systematic tension within rural economic actors’ accounts of climate change: farmers who relativize climate change as a personal or immediate concern simultaneously describe substantial adaptation measures already underway. We argue that this tension is not a problem of knowledge deficit or attitudinal lag, but a structurally necessary form of ideation: climate imaginaries hold contradictions in suspension precisely because acknowledging their depth would destabilize the identities, market dependencies, and broader intergenerational obligations on which rural economies frequently depend. Drawing on qualitative interviews with farmers in rural Luxembourg andapplyinga dialectical analytical framework, the article traces howclimate imaginaries differ with forms of market embeddedness, as do thecapacities to make transformation thinkable. The findings are developed in conversation with innovation studies in geography, specifying the ideational conditions under which structural opportunity translates, or fails to translate, into transformative action. The article contributes to debates on climate change, sustainability transformation and the growth-critical debate in economic geography, arguing, as a theoretical interpretation of these patterns, that the persistent shortcomings of market-oriented climate adaptation point to structural rather than merely contingent limits, and that making those limits analytically visible is a precondition for moving beyond them.
Large Language Models (LLM) have experienced strong development in recent years, with varied applications. This paper uses LLMs to develop a post-hoc process that provides more elaborated explanations of the results of food recommendation systems. By combining LLM with a hybrid extraction of key variables using SHAP, we obtain dynamic, convincing and more comprehensive explanations to lay user, compared to those in the literature. This approach enhances user trust and transparency by making complex recommendation outcomes easier to understand for a lay user.
Research summary We assess how personality alignment in investor-founder dyads is associated with the likelihood of follow-on funding, a vital outcome for early-stage ventures. Using machine learning to infer the Big Five personality traits from Twitter data for 9497 business angel-founder dyads, we find that similarity in conscientiousness and agreeableness is associated with a higher likelihood of follow-on funding, while similarity in neuroticism is associated with a lower likelihood. We attribute these patterns to the trait-specific benefits of supplementary (conscientiousness and agreeableness) and complementary (neuroticism) fit. Robustness checks and additional analyses support and nuance the conclusion that personality fit matters for venture outcomes, highlighting the strategic role of personality fit in the investor-founder relationship.Managerial summary We show that personality similarity between business angels and founders is associated with whether a venture secures follow-on funding. Similarity in conscientiousness (being organized and reliable) and agreeableness (being cooperative and trusting) is linked to a higher likelihood of securing follow-on funding, while similarity in neuroticism (emotional instability) is linked to a lower likelihood. We interpret these patterns as collaboration dynamics: similarity can help through supplementary fit (e.g., shared work style and cooperation), but differences can help through complementary fit (e.g., one partner's emotional stability offsets the other's emotional instability). Practically, founders and business angels should develop self-awareness and consider personality fit alongside other characteristics when forming partnerships. Policymakers and incubators can support better matches and strengthen collaboration by promoting awareness of interpersonal dynamics.
