Design, Actuation, and Functionalization of Untethered Soft Magnetic Robots with Life-Like Motions: A Review

Soft robots have demonstrated superior flexibility and functionality than conventional rigid robots. These versatile devices can respond to a wide range of external stimuli (including light, magnetic field, heat, electric field, etc.), and can perform sophisticated tasks. Notably, soft magnetic robots exhibit unparalleled advantages among numerous soft robots (such as untethered control, rapid response, and high safety), and have made remarkable progress in small-scale manipulation tasks and biomedical applications. Despite the promising potential, soft magnetic robots are still in their infancy and require significant advancements in terms of fabrication, design principles, and functional development to be viable for real-world applications. Recent progress shows that bionics can serve as an effective tool for developing soft robots. In light of this, the review is presented with two main goals: (i) exploring how innovative bioinspired strategies can revolutionize the design and actuation of soft magnetic robots to realize various life-like motions; (ii) examining how these bionic systems could benefit practical applications in small-scale solid/liquid manipulation and therapeutic/diagnostic-related biomedical fields

Subequivariant Graph Reinforcement Learning in 3D Environments

Learning a shared policy that guides the locomotion of different agents is of core interest in Reinforcement Learning (RL), which leads to the study of morphology-agnostic RL. However, existing benchmarks are highly restrictive in the choice of starting point and target point, constraining the movement of the agents within 2D space. In this work, we propose a novel setup for morphology-agnostic RL, dubbed Subequivariant Graph RL in 3D environments (3D-SGRL). Specifically, we first introduce a new set of more practical yet challenging benchmarks in 3D space that allows the agent to have full Degree-of-Freedoms to explore in arbitrary directions starting from arbitrary configurations. Moreover, to optimize the policy over the enlarged state-action space, we propose to inject geometric symmetry, i.e., subequivariance, into the modeling of the policy and Q-function such that the policy can generalize to all directions, improving exploration efficiency. This goal is achieved by a novel SubEquivariant Transformer (SET) that permits expressive message exchange. Finally, we evaluate the proposed method on the proposed benchmarks, where our method consistently and significantly outperforms existing approaches on single-task, multi-task, and zero-shot generalization scenarios. Extensive ablations are also conducted to verify our design. Code and videos are available on our project page: https://alpc91.github.io/SGRL/.Comment: ICML 2023 Ora

Treatment Strategy and Research Progress of Immune Microenvironment for Liver Metastasis of Non-small Cell Lung Cancer

Liver is the common site for metastasis and spread of non-small cell lung cancer (NSCLC). Lung cancer patients with liver metastasis have poor prognosis, which may be related to liver-specific microenvironment composition. The metastasis of lung cancer to the liver is regulated by various pathophysiological factors, including the liver immune microenvironment, related cells, proteins, signaling molecules, and gene changes. These factors will affect the consistent disease process and subsequent treatment strategies. Immune checkpoint inhibitors (ICIs) have made breakthroughs in treatment of patients with advanced NSCLC. However, NSCLC patients with liver metastasis, a unique population of advanced lung cancer, are characterized by poor immunotherapeutic effect. This paper reviews the related mechanisms of the immune microenvironment in affecting the occurrence and development of liver metastases and summarizes the achievements and prospects of anti-tumor immunotherapy in liver metastases of NSCLC

Intelligent Agents for Negotiation and Recommendation in Mass Customization

Mass customization, as a means to meet individual consumer’s need on a large scale, has recently attracted the attention of both researchers and practitioners. However, as customers and their needs grow increasingly diverse, meeting every consumer’s need has become a surefire way to add unnecessary cost and complexity to operations. Furthermore, consumers are not all really ready for mass customization. They have to face inconveniences such as expensive price, delay delivery and they have to spend time “designing” their product. In order to solve this problem, we proposed a way of intelligent agent assisted negotiation and recommendation. The recommendation is a preference elicitation process, while the negotiation is a communication process based on the situation of manufacturer, such as the inventory level, production cost and lead time. With the aid of intelligent agent of negotiation and recommendation, a good balance between efficiency and customer satisfactions of mass customization can be reached

Teaching Autonomous Vehicles to Express Interaction Intent during Unprotected Left Turns: A Human-Driving-Prior-Based Trajectory Planning Approach

Incorporating Autonomous Vehicles (AVs) into existing transportation systems necessitates examining their coexistence with Human-driven Vehicles (HVs) in mixed traffic environments. Central to this coexistence is the AVs' ability to emulate human-like interaction intentions within traffic scenarios. We introduce a novel framework for planning unprotected left-turn trajectories for AVs, designed to mirror human driving behaviors and effectively communicate social intentions. This framework consists of three phases: trajectory generation, evaluation, and selection.In the trajectory generation phase, we utilize real human-driving trajectory data to establish constraints for a predicted trajectory space, creating candidate motion trajectories that reflect intent. The evaluation phase incorporates maximum entropy inverse reinforcement learning (ME-IRL) to gauge human trajectory preferences, considering aspects like traffic efficiency, driving comfort, and interactive safety. During the selection phase, a Boltzmann distribution-based approach is employed to assign rewards and probabilities to the candidate trajectories, promoting human-like decision-making. We validate our framework using an authentic trajectory dataset and conduct a comparative analysis with various baseline methods. Our results, derived from simulator tests and human-in-the-loop driving experiments, affirm our framework's superiority in mimicking human-like driving, expressing intent, and computational efficiency. For additional information of this research, please visit https://shorturl.at/jqu35

Fault diagnosis for PV arrays considering dust impact based on transformed graphical feature of characteristic curves and convolutional neural network with CBAM modules

Various faults can occur during the operation of PV arrays, and both the dust-affected operating conditions and various diode configurations make the faults more complicated. However, current methods for fault diagnosis based on I-V characteristic curves only utilize partial feature information and often rely on calibrating the field characteristic curves to standard test conditions (STC). It is difficult to apply it in practice and to accurately identify multiple complex faults with similarities in different blocking diodes configurations of PV arrays under the influence of dust. Therefore, a novel fault diagnosis method for PV arrays considering dust impact is proposed. In the preprocessing stage, the Isc-Voc normalized Gramian angular difference field (GADF) method is presented, which normalizes and transforms the resampled PV array characteristic curves from the field including I-V and P-V to obtain the transformed graphical feature matrices. Then, in the fault diagnosis stage, the model of convolutional neural network (CNN) with convolutional block attention modules (CBAM) is designed to extract fault differentiation information from the transformed graphical matrices containing full feature information and to classify faults. And different graphical feature transformation methods are compared through simulation cases, and different CNN-based classification methods are also analyzed. The results indicate that the developed method for PV arrays with different blocking diodes configurations under various operating conditions has high fault diagnosis accuracy and reliability

Prior Bilinear Based Models for Knowledge Graph Completion

Bilinear based models are powerful and widely used approaches for Knowledge Graphs Completion (KGC). Although bilinear based models have achieved significant advances, these studies mainly concentrate on posterior properties (based on evidence, e.g. symmetry pattern) while neglecting the prior properties. In this paper, we find a prior property named "the law of identity" that cannot be captured by bilinear based models, which hinders them from comprehensively modeling the characteristics of KGs. To address this issue, we introduce a solution called Unit Ball Bilinear Model (UniBi). This model not only achieves theoretical superiority but also offers enhanced interpretability and performance by minimizing ineffective learning through minimal constraints. Experiments demonstrate that UniBi models the prior property and verify its interpretability and performance

The Adoption of Blockchain Technologies in Data Sharing: A State of the Art Survey

In the big data era, it is a significant need for data sharing in various industries. However, there are many weaknesses in the traditional centralized way of data sharing. It is easy to attack the centralized data storage center. As the process of data asset transactions is not transparent, there is a lack of trust in the percipients of data sharing. Blockchain technology offers a possibility to solve these problems in data sharing, as the blockchain can provide a decentralized, programmable, tamperproof, and anonymous data sharing environment. In this paper, we compare the blockchain-based data sharing with the traditional ways of data sharing, and analyze the scenarios in major industry applications. We survey the state of the art of the adoption of blockchain technologies in data sharing, and provide a summary about their technical frameworks and schemes

DDM-Lag : A Diffusion-based Decision-making Model for Autonomous Vehicles with Lagrangian Safety Enhancement

Decision-making stands as a pivotal component in the realm of autonomous vehicles (AVs), playing a crucial role in navigating the intricacies of autonomous driving. Amidst the evolving landscape of data-driven methodologies, enhancing decision-making performance in complex scenarios has emerged as a prominent research focus. Despite considerable advancements, current learning-based decision-making approaches exhibit potential for refinement, particularly in aspects of policy articulation and safety assurance. To address these challenges, we introduce DDM-Lag, a Diffusion Decision Model, augmented with Lagrangian-based safety enhancements. This work conceptualizes the sequential decision-making challenge inherent in autonomous driving as a problem of generative modeling, adopting diffusion models as the medium for assimilating patterns of decision-making. We introduce a hybrid policy update strategy for diffusion models, amalgamating the principles of behavior cloning and Q-learning, alongside the formulation of an Actor-Critic architecture for the facilitation of updates. To augment the model's exploration process with a layer of safety, we incorporate additional safety constraints, employing a sophisticated policy optimization technique predicated on Lagrangian relaxation to refine the policy learning endeavor comprehensively. Empirical evaluation of our proposed decision-making methodology was conducted across a spectrum of driving tasks, distinguished by their varying degrees of complexity and environmental contexts. The comparative analysis with established baseline methodologies elucidates our model's superior performance, particularly in dimensions of safety and holistic efficacy