Magnetic Interaction between Surface Engineered Rare-earth Atomic Spins

We report the ab initio study of rare-earth adatoms (Gd) on an insulating surface. This surface is of interest because of previous studies by scanning tunneling microscopy showing spin excitations of transition metal adatoms. The present work is the first study of rare-earth spin-coupled adatoms, as well as the geometry effect of spin coupling, and the underlying mechanism of ferromagnetic coupling. The exchange coupling between Gd atoms on the surface is calculated to be antiferromagnetic in a linear geometry and ferromagnetic in a diagonal geometry, by considering their collinear spins and using the PBE+U exchange correlation. We also find the Gd dimers in these two geometries are similar to the nearest-neighbor (NN) and the next-NN Gd atoms in GdN bulk. We analyze how much direct exchange, superexchange, and RKKY interactions contribute to the exchange coupling for both geometries by additional first-principles calculations of related model systems

Experimental and simulation study on nonlinear pitch control of Seagull underwater glider

1008-1015The Seagull underwater glider, developed by the Shanghai Jiao Tong University, is designed as a test-bed glider for the development and validation of various algorithms to enhance the glider’s long-term autonomy. In this paper, an adaptive backstepping control (ABC) method is proposed for the nonlinear pitch control of the underwater glider gliding in the vertical plane. The linear quadratic regulator (LQR) control and proportional-integral-derivative (PID) control are applied and evaluated with the ABC method to control a glider in saw-tooth motion. Simulation results demonstrate inherent effectiveness and superiority of the LQR or PID based method. According to Lyapunov stability theory, the ABC control scheme is derived to ensure the tracking errors asymptotically converge to zero. The ABC controller has been implemented on Seagull underwater glider, and verified in field experiments in the Qiandao Lake, Zhejiang

RecExplainer: Aligning Large Language Models for Recommendation Model Interpretability

Recommender systems are widely used in various online services, with embedding-based models being particularly popular due to their expressiveness in representing complex signals. However, these models often lack interpretability, making them less reliable and transparent for both users and developers. With the emergence of large language models (LLMs), we find that their capabilities in language expression, knowledge-aware reasoning, and instruction following are exceptionally powerful. Based on this, we propose a new model interpretation approach for recommender systems, by using LLMs as surrogate models and learn to mimic and comprehend target recommender models. Specifically, we introduce three alignment methods: behavior alignment, intention alignment, and hybrid alignment. Behavior alignment operates in the language space, representing user preferences and item information as text to learn the recommendation model's behavior; intention alignment works in the latent space of the recommendation model, using user and item representations to understand the model's behavior; hybrid alignment combines both language and latent spaces for alignment training. To demonstrate the effectiveness of our methods, we conduct evaluation from two perspectives: alignment effect, and explanation generation ability on three public datasets. Experimental results indicate that our approach effectively enables LLMs to comprehend the patterns of recommendation models and generate highly credible recommendation explanations.Comment: 12 pages, 8 figures, 4 table

Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations

Recommender models excel at providing domain-specific item recommendations by leveraging extensive user behavior data. Despite their ability to act as lightweight domain experts, they struggle to perform versatile tasks such as providing explanations and engaging in conversations. On the other hand, large language models (LLMs) represent a significant step towards artificial general intelligence, showcasing remarkable capabilities in instruction comprehension, commonsense reasoning, and human interaction. However, LLMs lack the knowledge of domain-specific item catalogs and behavioral patterns, particularly in areas that diverge from general world knowledge, such as online e-commerce. Finetuning LLMs for each domain is neither economic nor efficient. In this paper, we bridge the gap between recommender models and LLMs, combining their respective strengths to create a versatile and interactive recommender system. We introduce an efficient framework called InteRecAgent, which employs LLMs as the brain and recommender models as tools. We first outline a minimal set of essential tools required to transform LLMs into InteRecAgent. We then propose an efficient workflow within InteRecAgent for task execution, incorporating key components such as a memory bus, dynamic demonstration-augmented task planning, and reflection. InteRecAgent enables traditional recommender systems, such as those ID-based matrix factorization models, to become interactive systems with a natural language interface through the integration of LLMs. Experimental results on several public datasets show that InteRecAgent achieves satisfying performance as a conversational recommender system, outperforming general-purpose LLMs.Comment: 16 pages, 15 figures, 4 table

Dynamic modeling and optimal control of a positive buoyancy diving autonomous vehicle

The positive buoyancy diving autonomous vehicle combines the features of an Unmanned Surface Vessel (USV) and an Autonomous Underwater Vehicle (AUV) for marine measurement and monitoring. It can also be used to study reasonable and efficient positive buoyancy diving techniques for underwater robots. In order to study the optimization of low power consumption and high efficiency cruise motion of the positive buoyancy diving vehicle, its dynamic modeling has been established. The optimal cruising speed for low energy consumption of the positive buoyancy diving vehicle is determined by numerical simulation. The Linear Quadratic Regulator (LQR) controller is designed to optimize the dynamic error and the actuator energy consumption of the vehicle in order to achieve the optimal fixed depth tracking control of the positive buoyancy diving vehicle. The results demonstrate that the LQR controller has better performance than PID, and the system adjustment time of the LQR controller is reduced by approximately 56% relative to PID. The motion optimization control method proposed can improve the endurance of the positive buoyancy diving vehicle, and has a certain application value

MEDL-U: Uncertainty-aware 3D Automatic Annotation based on Evidential Deep Learning

Advancements in deep learning-based 3D object detection necessitate the availability of large-scale datasets. However, this requirement introduces the challenge of manual annotation, which is often both burdensome and time-consuming. To tackle this issue, the literature has seen the emergence of several weakly supervised frameworks for 3D object detection which can automatically generate pseudo labels for unlabeled data. Nevertheless, these generated pseudo labels contain noise and are not as accurate as those labeled by humans. In this paper, we present the first approach that addresses the inherent ambiguities present in pseudo labels by introducing an Evidential Deep Learning (EDL) based uncertainty estimation framework. Specifically, we propose MEDL-U, an EDL framework based on MTrans, which not only generates pseudo labels but also quantifies the associated uncertainties. However, applying EDL to 3D object detection presents three primary challenges: (1) relatively lower pseudolabel quality in comparison to other autolabelers; (2) excessively high evidential uncertainty estimates; and (3) lack of clear interpretability and effective utilization of uncertainties for downstream tasks. We tackle these issues through the introduction of an uncertainty-aware IoU-based loss, an evidence-aware multi-task loss function, and the implementation of a post-processing stage for uncertainty refinement. Our experimental results demonstrate that probabilistic detectors trained using the outputs of MEDL-U surpass deterministic detectors trained using outputs from previous 3D annotators on the KITTI val set for all difficulty levels. Moreover, MEDL-U achieves state-of-the-art results on the KITTI official test set compared to existing 3D automatic annotators.Comment: 6 pages Main, 1 page Reference, 5 pages Appendi