Follow Me:A Study on the Dynamics of Alignment Between Humans and LLM-Based Social Robots

While robots are perceived as reliable in delivering factual data, their ability to achieve meaningful alignment with humans during subjective interactions remains unclear. Gaining insights into this alignment is vital to integrating robots more deeply into decision-making frameworks and enhancing their roles in social interactions. This study examines the impact of personality-prompted large language models (LLMs) on alignment in human-robot interactions. Participants interacted with a Furhat robot under two conditions: a baseline control condition and an experimental condition using personality prompts designed to simulate distinct personality traits through the LLM. Alignment was assessed by measuring changes in similarity between participants’ rankings and the robot’s rankings of factual (objective) and contestable (subjective) concepts before and after interaction. The findings indicate that participants aligned more with the robot on objective, factual concepts than on subjective, contestable ones, regardless of personality prompts. These results suggest that the current personality prompting method may be insufficient to significantly influence alignment in subjective interactions. This may be attributed to the conveyed traits lacking sufficient impact or the limitations of current system capabilities, which may not yet be advanced enough to foster the desired influence on participants’ perceptions.</p

Self-Supervised Learning for Pre-training Capsule Networks: Overcoming Medical Imaging Dataset Challenges

Deep learning techniques are increasingly being adopted in diagnostic medical imaging. However, the limited availability of high-quality, large-scale medical datasets presents a significant challenge, often necessitating the use of transfer learning approaches. This study investigates self-supervised learning methods for pre-training capsule networks in networks in polyp diagnostics for colon cancer. We used the PICCOLO dataset, comprising 3,443 samples, which exemplifies typical challenges in medical datasets: small size, class imbalance, and distribution shifts between data splits. Capsule networks offer inherent interpretability due to their architecture and inter-layer information routing mechanism. However, their limited native implementation in mainstream deep learning frameworks and the lack of pre-trained versions pose a significant challenge. This is particularly true if aiming to train them on small medical datasets, where leveraging pre-trained weights as initial parameters would be beneficial. We explored two auxiliary self-supervised learning tasks -- colourisation and contrastive learning -- for capsule network pre-training. We compared self-supervised pre-trained models against alternative initialisation strategies. Our findings suggest that contrastive learning in-painting techniques are suitable auxiliary tasks for self-supervised learning in the medical domain. These techniques helped guide the model to capture important visual features that are beneficial for the downstream task of polyp classification, increasing its accuracy by 5.26% compared to other weight initialisation methods

Storage v. production: challenges for reservoir modelling and simulation practitioners

The rising interest in subsurface CO2 storage makes new calls on reservoir modelling skills, most of which have been developed for hydrocarbon production scenarios. The question for practitioners is: to what extent can the familiar production tools be transferred to the world of storage? In this paper, areas requiring attention are highlighted and high-resolution models are used to compare the behaviour of simulators for production v. storage for two reservoir analogue examples. It is concluded that modelling for storage makes a significant call on multi-scale modelling, to a much greater extent than in production scenarios, and the simplification or omission of reservoir heterogeneities (sometimes tolerable in production scenarios) are much less tolerable when modelling storage. Key static model heterogeneities include the modelling of faults as 3D features, the inclusion of fine-scale reservoir permeability contrasts and the avoidance of net reservoir cut-offs. For dynamic models, use of equation of state is necessary for storage in depleted fields, and correct representation of hysteretic effects of plume migration are a requirement for modelling in aquifers (always) and depleted fields (usually). Modelling for storage, especially for saline aquifers, sets the challenge of modelling volumes previously considered to be at exploration scale, but with an effective resolution more typical of production scales

Follow Me:A Study on the Dynamics of Alignment Between Humans and LLM-Based Social Robots

While robots are perceived as reliable in delivering factual data, their ability to achieve meaningful alignment with humans during subjective interactions remains unclear. Gaining insights into this alignment is vital to integrating robots more deeply into decision-making frameworks and enhancing their roles in social interactions. This study examines the impact of personality-prompted large language models (LLMs) on alignment in human-robot interactions. Participants interacted with a Furhat robot under two conditions: a baseline control condition and an experimental condition using personality prompts designed to simulate distinct personality traits through the LLM. Alignment was assessed by measuring changes in similarity between participants’ rankings and the robot’s rankings of factual (objective) and contestable (subjective) concepts before and after interaction. The findings indicate that participants aligned more with the robot on objective, factual concepts than on subjective, contestable ones, regardless of personality prompts. These results suggest that the current personality prompting method may be insufficient to significantly influence alignment in subjective interactions. This may be attributed to the conveyed traits lacking sufficient impact or the limitations of current system capabilities, which may not yet be advanced enough to foster the desired influence on participants’ perceptions.</p

Experimental Evaluation of Pore Proximity Effects on Gas and Condensate Phase Behaviour in Real Reservoirs: Implications of Fluid Composition

Unconventional reservoirs can play a pivotal role in meeting the growing energy demand and compensating for the decline in production from conventional hydrocarbon resources. However, the flow and phase behaviours of fluids within these porous media are not yet fully comprehended. This study investigates the impact of fluid confinement, small pore spaces, and pore walls on gas-condensate systems within real unconventional rocks. A novel experimental workflow was developed to measure the effective dew point pressure (Pdew) of various gas-condensate mixtures in different real rock samples. The proposed workflow, for the first time allows to compare the Pdew of gas condensate mixtures in bulk and inside porous media. The research reveals that pore confinement increases Pdew of gas condensate mixtures while reducing it in single-component fluids. The concentration of heavier components significantly influenced Pdew under the influence of confinement. For the fluid mixtures used in this study, pore confinement effects increased the Pdew by around 25 psi for the mixture with the lightest components and by more than 250 psi for the mixture with the highest concentration of heavier components. These experimental findings enhance our understanding of fluid phase behaviour under pore confinement conditions and emphasize the significant role of heavier component concentration and type. The results provide valuable data for validating theoretical methods and practical applications in estimating confinement effects in unconventional gas-condensate reservoirs. This research supports the development of modified equations of state (EOS) to describe the behaviour of gas condensate mixtures under confinement, contributing significantly to energy resource management and the effective exploitation of unconventional reservoirs

Towards Autonomous Subsea Longitudinal Object Detection and Tracking Using a Multi-beam Echo-Sounder

Subsea pipelines and cables are critical assets which require regular maintenance and inspection to ensure their integrity and continual operation. The autonomous tracking of these assets requires robust and reliable methods especially in the challenging subsea environment. This paper presents a new method for the robust autonomous detection and tracking of subsea pipelines and cables using a multi-beam echo-sounder sensor, leveraging intensity and profiling returns for enhanced robustness. The proposed method involves four key steps. First, prepocessing operations are carried out to refine the raw sensor data, followed by a region of interest generation using the K-means clustering algorithm, then a validation step which filters implausable regions and finally a fitting processes for determining the target's position and parameters. The proposed method is also designed to extend the detection and tracking capabilities of the system to the 3-dimensional use case. Through real-world and simulated experiments we demonstrate the effectiveness of the method

Comparative Evaluation of Reinforcement Learning and Model Predictive Control for 6DoF Position Control of an Autonomous Underwater Vehicle

Autonomous Underwater Vehicles (AUVs) require precise and robust control strategies for 3D pose regulation in dynamic underwater environments. In this study, we present a comparative evaluation of model-free and model-based control methods for AUV position control. Specifically, we analyze the performance of neural network controllers trained by three Reinforcement Learning (RL) algorithms---Proximal Policy Optimization (PPO), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Soft Actor-Critic (SAC)---alongside a Model Predictive Control (MPC) baseline. We train our RL methods in a simplified AUV simulator implemented in PyTorch, while our evaluation is done in a realistic marine robotics simulator called Stonefish. Controllers are evaluated on the basis of tracking accuracy, robustness to disturbances, and generalization capabilities. Our results show that, MPC suffers from unmodeled dynamics such as disturbances, whereas RL demonstrates adaptation capabilities to disturbances. Also, although MPC demonstrates strong control performance, it requires an accurate model, high compute power and a careful implementation to run in real-time whereas the control frequency of RL policies is only bound by the inference time of the policy network. Among RL-based controllers, PPO achieves the best overall performance, both in terms of training stability and control accuracy. This study provides insight into the feasibility of RL-based controllers for AUV position control, offering guidance for selecting suitable control strategies in real-world marine robotics applications

Optimal Designs of the Group Runs Exponentially Weighted Moving Average X̄ and <i>t </i>Schemes

The analysis of an X̄ scheme often assumes that the process standard deviation is accurately assessed and remains constant. However, in practice, this is rarely true. Noting that the group runs (GR) scheme performs better than the synthetic scheme, in this research, we proposed the GR exponentially weighted moving average GR EWMA X̄ and t schemes and determined their true optimal parameters using the optimisation programmes. Our findings indicate that similar to the synthetic EWMA X̄ scheme, the proposed GR EWMA X̄ scheme is not resilient to errors in the estimation of the standard deviation of the process or when the standard deviation changes. Therefore, we also proposed the GR EWMA t scheme for surveilling the mean of a process. We demonstrate that this t scheme possesses the required robust characteristic. We showcase our developed schemes’ superiority over existing schemes in a detailed performance comparison. An illustrative example related to the hard-baking process is utilised to demonstrate the applicability of the suggested schemes

Emerging patents versus brain eating amoebae, Naegleria fowleri

Primary Amoebic Meningoencephalitis (PAM) is a severe and often fatal infection caused by the free-living amoebae Naegleria fowleri. This condition typically results from exposure to contaminated warm freshwater/inadequately treated recreational water/or ablution/nasal irrigation with contaminated water. The management of PAM is hindered by the absence of effective treatment coupled with challenges in early diagnosis. This review explores emerging patents that could be utilized for the treatment, diagnosis of PAM, as well as water treatment. Recent patents from the past five years, along with research and innovations are reviewed and categorized into therapeutic agents, water treatment technologies, and diagnostic methods. It is hoped that collaboration and awareness between pharmaceutical companies, water industries, and academic institutions is essential for advancing effective strategies against this severe central nervous system pathogen.</p

Heat method extensions for distance function estimation in planar and space domains

Given a bounded domain, we deal with the problem of estimating the distance function from the internal points of the domain to the boundary of the domain. Two simple extensions of the heat method for distance computation are introduced and evaluated. The extensions are based on first- and second-order Taylor series extrapolations. Numerical experiments demonstrate that the extensions deliver more accurate and robust estimates of the distance function