Recently, ChatGPT, a representative large language model (LLM), has gained considerable attention. Due to their powerful emergent abilities, recent LLMs are considered as a possible alternative to structured knowledge bases like knowledge graphs (KGs). However, while LLMs are proficient at learning probabilistic language patterns and engaging in conversations with humans, they, like previous smaller pre-trained language models (PLMs), still have difficulty in recalling facts while generating knowledge-grounded contents. To overcome these limitations, researchers have proposed enhancing data-driven PLMs with knowledge-based KGs to incorporate explicit factual knowledge into PLMs, thus improving their performance in generating texts requiring factual knowledge and providing more informed responses to user queries. This paper reviews the studies on enhancing PLMs with KGs, detailing existing knowledge graph enhanced pre-trained language models (KGPLMs) as well as their applications. Inspired by existing studies on KGPLM, this paper proposes enhancing LLMs with KGs by developing knowledge graph-enhanced large language models (KGLLMs). KGLLM provides a solution to enhance LLMs’ factual reasoning ability, opening up new avenues for LLM research.

Autonomous driving has so far received numerous attention from academia and industry. However, the inevitable occlusion is a great menace to safety and reliable driving. Existing works have primarily focused on improving the perception ability of a single autonomous vehicle (AV), but the safety problem brought by occlusions remains unanswered. In this paper, we propose a multi-tier perception task offloading framework with a collaborative computing approach, where an AV is able to achieve a comprehensive perception of the concerned region-of-interest (RoI) by leveraging collaborative computation with nearby AVs and road side units (RSUs). Besides, the collaborative computation provides offloading service for computationally intensive tasks so as to reduce processing delay. Specifically, we formulate a joint problem of perception task assignment, offloading and resource allocation, by fully considering the AV’s mobility, task dependency, and delay requirement. The collaborative offloading is modeled as a mixed-integer nonlinear programming (MINLP) problem. We design a two-layer binary intelligent firefly (TL-BIFA) algorithm to solve MINLP, with the goal of minimizing execution delay. The proposed TL-BIFA synthesizes the advantages of heuristic methods and deterministic methods. Through extensive simulations, the proposed collaborative offloading approach and the TL-BIFA show superiority in enhancing the autonomous driving system’s safety, efficiency and resource utilization.

Large Language Models (LLMs) have emerged as a transformative power in enhancing natural language comprehension, representing a significant stride toward artificial general intelligence. The application of LLMs extends beyond conventional linguistic boundaries, encompassing specialized linguistic systems developed within various scientific disciplines. This growing interest has led to the advent of scientific LLMs, a novel subclass specifically engineered for facilitating scientific discovery. As a burgeoning area in the community of AI for Science, scientific LLMs warrant comprehensive exploration. However, a systematic and up-to-date survey introducing them is currently lacking. In this article, we endeavor to methodically delineate the concept of “scientific language,” whilst providing a thorough review of the latest advancements in scientific LLMs. Given the expansive realm of scientific disciplines, our analysis adopts a focused lens, concentrating on the biological and chemical domains. This includes an in-depth examination of LLMs for textual knowledge, small molecules, macromolecular proteins, genomic sequences, and their combinations, analyzing them in terms of model architectures, capabilities, datasets, and evaluation. Finally, we critically examine the prevailing challenges and point out promising research directions along with the advances of LLMs. By offering a comprehensive overview of technical developments in this field, this survey aspires to be an invaluable resource for researchers navigating the intricate landscape of scientific LLMs.

Understanding the private car aggregation effect is conducive to a broad range of applications, from intelligent transportation management to urban planning. However, this work is challenging, especially on weekends, due to the inefficient representations of spatiotemporal features for such aggregation effect and the considerable randomness of private car mobility on weekends. In this article, we propose a deep learning framework for a spatiotemporal attention network (STANet) with a neural algorithm logic unit (NALU), the so-called STANet-NALU, to understand the dynamic aggregation effect of private cars on weekends. Specifically: 1) we design an improved kernel density estimator (KDE) by defining a log-cosh loss function to calculate the spatial distribution of the aggregation effect with guaranteed robustness and 2) we utilize the stay time of private cars as a temporal feature to represent the nonlinear temporal correlation of the aggregation effect. Next, we propose a spatiotemporal attention module that separately captures the dynamic spatial correlation and nonlinear temporal correlation of the private car aggregation effect, and then we design a gate control unit to fuse spatiotemporal features adaptively. Further, we establish the STANet-NALU structure, which provides the model with numerical extrapolation ability to generate promising prediction results of the private car aggregation effect on weekends. We conduct extensive experiments based on real-world private car trajectories data. The results reveal that the proposed STANet-NALU outperforms the well-known existing methods in terms of various metrics, including the mean absolute error (MAE), root mean square error (RMSE), Kullback–Leibler divergence (KL), and R2.

Mobile Edge Computing (MEC) has been witnessed as a promising solution for the vehicular task offloading. Due to the limited computing resource of individual MEC servers, it faces challenges when higher requirements are put forward for timely task processing of large amount of computations in the emerging vehicular applications. In this paper, we strive to realize the efficient vehicular task offloading via heat-aware MEC cooperation from the game theory perspective. Here the heat indicates the vehicle density and is tightly related to the requests of vehicle users when they drive through the hotzones. Specifically, a deep learning based prediction method is proposed, capturing the dynamic time-varying heat value of the hot zones based on the analysis of the real-world private car trajectory data. To identify the role of MEC in the cooperation, we take the time-delay constraint into consideration for the task offloading. To realize MEC grouping for task offloading in MEC cooperation, we formulate the MEC grouping as a utility maximization problem via designing a non-cooperative game-theoretic strategy selection based on regret-matching. Furthermore, we derive the correlated equilibrium and prove that the fast convergence can be achieved. Extensive simulation results validate the effectiveness of the proposed vehicular task offloading approach under various system parameters such as computation workload, time slots, and MEC servers number. The proposed method outperforms the existing methods, which is able to significantly reduce the task complete delay, and in the meantime enhance the MEC energy efficiency with end-users’ QoE guaranteed.

Over the last decade, machine learning (ML) and deep learning (DL) algorithms have significantly evolved and been employed in diverse applications, such as computer vision, natural language processing, automated speech recognition, etc. Real-time safety-critical embedded and Internet of Things (IoT) systems, such as autonomous driving systems, UAVs, drones, security robots, etc., heavily rely on ML/DL-based technologies, accelerated with the improvement of hardware technologies. The cost of a deadline (required time constraint) missed by ML/DL algorithms would be catastrophic in these safety-critical systems. However, ML/DL algorithm-based applications have more concerns about accuracy than strict time requirements. Accordingly, researchers from the real-time systems (RTSs) community address the strict timing requirements of ML/DL technologies to include in RTSs. This article will rigorously explore the state-of-the-art results emphasizing the strengths and weaknesses in ML/DL-based scheduling techniques, accuracy versus execution time tradeoff policies of ML algorithms, and security and privacy of learning-based algorithms in real-time IoT systems.