Polycrystal plasticity in metals is characterized by nonlinear behavior and strain hardening, making numerical models computationally intensive. We employ Graph Neural Network (GNN) to surrogate polycrystal plasticity from finite element method (FEM) simulations. We present a novel message-passing GNN that encodes nodal strain and edge distances between FEM mesh cells, aggregates them to obtain embeddings, and combines the decoded embeddings with the nodal strains to predict stress tensors on graph nodes. We demonstrate training GNN based on subgraphs generated from FEM mesh-graphs, in which the mesh cells are converted to nodes and edges are created between adjacent cells. The GNN is trained on 72 graphs and tested on 18 graphs. We apply the trained GNN to periodic polycrystals and learn the stress-strain maps based on strain-gradient plasticity theory. The GNN is accurately trained based on FEM graphs, in which the $R^2$ for both training and testing sets are 0.993. The proposed GNN plasticity constitutive model speeds up more than 150 times compared with the benchmark FEM method on randomly selected test polycrystals. We also apply the trained GNN to 30 unseen FEM simulations and the GNN generalizes well with an overall $R^2$ of 0.992. Analysis of the von Mises stress distributions in polycrystals shows that the GNN model accurately learns the stress distribution with low error. By comparing the error distribution across training, testing, and unseen datasets, we can deduce that the proposed model does not overfit and generalizes well beyond the training data. This work is expected to pave the way for using graphs as surrogates in polycrystal plasticity modeling.

This study presents an explainable artificial intelligent (XAI) model for predicting pile driving vibrations in Bangkok's soft clay subsoil. A deep neural network was developed using a dataset of 1,018 real-world pile driving measurements, encompassing variations in pile dimensions, hammer characteristics, sensor locations, and vibration measurement axes. The model achieved a mean absolute error (MAE) of 0.276, outperforming traditional empirical methods and other machine learning approaches such as XGBoost and CatBoost. SHapley Additive exPlanations (SHAP) analysis was employed to interpret the model's predictions, revealing complex relationships between input features and peak particle velocity (PPV). Distance from the pile driving location emerged as the most influential factor, followed by hammer weight and pile size. Non-linear relationships and threshold effects were observed, providing new insights into vibration propagation in soft clay. A web-based application was developed to facilitate adoption by practicing engineers, bridging the gap between advanced machine learning techniques and practical engineering applications. This research contributes to the field of geotechnical engineering by offering a more accurate and nuanced approach to predicting pile driving vibrations, with implications for optimizing construction practices and mitigating environmental impacts in urban areas. The model and its source code are publicly available, promoting transparency and reproducibility in geotechnical research.

Language model alignment research often attempts to ensure that models are not only helpful and harmless, but also truthful and unbiased. However, optimizing these objectives simultaneously can obscure how improving one aspect might impact the others. In this work, we focus on analyzing the relationship between two concepts essential in both language model alignment and political science: \textit{truthfulness} and \textit{political bias}. We train reward models on various popular truthfulness datasets and subsequently evaluate their political bias. Our findings reveal that optimizing reward models for truthfulness on these datasets tends to result in a left-leaning political bias. We also find that existing open-source reward models (i.e. those trained on standard human preference datasets) already show a similar bias and that the bias is larger for larger models. These results raise important questions about both the datasets used to represent truthfulness and what language models capture about the relationship between truth and politics.

The massive use of machine learning models, particularly neural networks, has raised serious concerns about their environmental impact. Indeed, over the last few years we have seen an explosion in the computing costs associated with training and deploying these systems. It is, therefore, crucial to understand their energy requirements in order to better integrate them into the evaluation of models, which has so far focused mainly on performance. In this paper, we study several neural network architectures that are key components of sound event detection systems, using an audio tagging task as an example. We measure the energy consumption for training and testing small to large architectures and establish complex relationships between the energy consumption, the number of floating-point operations, the number of parameters, and the GPU/memory utilization.

This paper presents an adaptive visual servoing framework for robotic on-orbit servicing (OOS), specifically designed for capturing tumbling satellites. The vision-guided robotic system is capable of selecting optimal control actions in the event of partial or complete vision system failure, particularly in the short term. The autonomous system accounts for physical and operational constraints, executing visual servoing tasks to minimize a cost function. A hierarchical control architecture is developed, integrating a variant of the Iterative Closest Point (ICP) algorithm for image registration, a constrained noise-adaptive Kalman filter, fault detection and recovery logic, and a constrained optimal path planner. The dynamic estimator provides real-time estimates of unknown states and uncertain parameters essential for motion prediction, while ensuring consistency through a set of inequality constraints. It also adjusts the Kalman filter parameters adaptively in response to unexpected vision errors. In the event of vision system faults, a recovery strategy is activated, guided by fault detection logic that monitors the visual feedback via the metric fit error of image registration. The estimated/predicted pose and parameters are subsequently fed into an optimal path planner, which directs the robot's end-effector to the target's grasping point. This process is subject to multiple constraints, including acceleration limits, smooth capture, and line-of-sight maintenance with the target. Experimental results demonstrate that the proposed visual servoing system successfully captured a free-floating object, despite complete occlusion of the vision system.

This paper introduces a novel approach to interactive robots by leveraging the form-factor of cards to create thin robots equipped with vibrational capabilities for locomotion and haptic feedback. The system is composed of flat-shaped robots with on-device sensing and wireless control, which offer lightweight portability and scalability. This research introduces a hardware prototype. Applications include augmented card playing, educational tools, and assistive technology, which showcase CARDinality's versatility in tangible interaction.

Reliability analysis is a formidable task, particularly in systems with a large number of stochastic parameters. Conventional methods for quantifying reliability often rely on extensive simulations or experimental data, which can be costly and time-consuming, especially when dealing with systems governed by complex physical laws which necessitates computationally intensive numerical methods such as finite element or finite volume techniques. On the other hand, surrogate-based methods offer an efficient alternative for computing reliability by approximating the underlying model from limited data. Neural operators have recently emerged as effective surrogates for modelling physical systems governed by partial differential equations. These operators can learn solutions to PDEs for varying inputs and parameters. Here, we investigate the efficacy of the recently developed physics-informed wavelet neural operator in solving reliability analysis problems. In particular, we investigate the possibility of using physics-informed operator for solving high-dimensional reliability analysis problems, while bypassing the need for any simulation. Through four numerical examples, we illustrate that physics-informed operator can seamlessly solve high-dimensional reliability analysis problems with reasonable accuracy, while eliminating the need for running expensive simulations.

The uncertainty inherent in the environmental transition model of Reinforcement Learning (RL) necessitates a careful balance between exploration and exploitation to optimize the use of computational resources for accurately estimating an agent's expected reward. Achieving balance in control systems is particularly challenging in scenarios with sparse rewards. However, given the extensive prior knowledge available for many environments, it is redundant to begin learning from scratch in such settings. To address this, we introduce \textbf{L}anguage \textbf{M}odel \textbf{G}uided \textbf{T}rade-offs (i.e., \textbf{LMGT}), a novel, sample-efficient framework that leverages the comprehensive prior knowledge embedded in Large Language Models (LLMs) and their adeptness at processing non-standard data forms, such as wiki tutorials. LMGT proficiently manages the exploration-exploitation trade-off by employing reward shifts guided by LLMs, which direct agents' exploration endeavors, thereby improving sample efficiency. We have thoroughly tested LMGT across various RL tasks and deployed it in industrial-grade RL recommendation systems, where it consistently outperforms baseline methods. The results indicate that our framework can significantly reduce the time cost required during the training phase in RL.

Fuel cells using oxygen and glucose could power microscopic robots operating in blood vessels. Swarms of such robots can significantly reduce oxygen concentration, depending on the time between successive transits of the lung, hematocrit variation in vessels and tissue oxygen consumption. These factors differ among circulation paths through the body. This paper evaluates how these variations affect the minimum oxygen concentration due to robot consumption and where it occurs: mainly in moderate-sized veins toward the end of long paths prior to their merging with veins from shorter paths. This shows that tens of billions of robots can obtain hundreds of picowatts throughout the body with minor reduction in total oxygen. However, a trillion robots significantly deplete oxygen in some parts of the body. By storing oxygen or limiting their consumption in long circulation paths, robots can actively mitigate this depletion. The variation in behavior is illustrated in three cases: the portal system which involves passage through two capillary networks, the spleen whose slits significantly slow some of the flow, and large tissue consumption in coronary circulation.

Modern machine-learning techniques are generally considered data-hungry. However, this may not be the case for turbulence as each of its snapshots can hold more information than a single data file in general machine-learning applications. This study asks the question of whether nonlinear machine-learning techniques can effectively extract physical insights even from as little as a single snapshot of a turbulent vortical flow. As an example, we consider machine-learning-based super-resolution analysis that reconstructs a high-resolution field from low-resolution data for two-dimensional decaying turbulence. We reveal that a carefully designed machine-learning model trained with flow tiles sampled from only a single snapshot can reconstruct vortical structures across a range of Reynolds numbers. Successful flow reconstruction indicates that nonlinear machine-learning techniques can leverage scale-invariance properties to learn turbulent flows. We further show that training data of turbulent flows can be cleverly collected from a single snapshot by considering characteristics of rotation and shear tensors. The present findings suggest that embedding prior knowledge in designing a model and collecting data is important for a range of data-driven analyses for turbulent flows. More broadly, this work hopes to stop machine-learning practitioners from being wasteful with turbulent flow data.