In Question Answering (QA), Retrieval Augmented Generation (RAG) has revolutionized performance in various domains.However, how to effectively capture multi-document relationships remains an open question.This is particularly critical for biomedical tasks due to their reliance on information spread across multiple documents.In this work, we propose a novel method CLAIMS, which utilizes propositional claims to construct a local knowledge graph from retrieved documents.Summaries are then derived via layerwise summarization from the knowledge graph to contextualize a small language model to perform QA.The structured summaries effectively capture explicit and implicit relationships between entities in the documents, thus having a more comprehensive context to provide to LLMs.CLAIMS achieved comparable or superior performance over RAG baselines on several biomedical QA benchmarks.We also evaluated its generalizability and each individual step of our approach with a targeted set of metrics, demonstrating its effectiveness.

3D object detection is a crucial task for autonomous vehicles to perceive traffic environments. Existing methods typically employ tightly coupled feature fusion strategies with fixed sensor combinations, but it fails to adequately capture the modality-specific characteristics, resulting in suboptimal object detection performance. To this end, we propose Chameleon, a novel multi-sensor decoupling system capable of selecting the appropriate sensor combination for object detection across diverse traffic conditions, which consists of two key components: (i) anuncertainty-aware contribution calculationcomponent leverages the uncertainty perceived by sensors to predict the parameters of traffic factors, followed by evaluating their importance; (ii) amutual information-enhanced sensor combination optimizationcomponent leverages mutual information calculations to enhance the mixture of experts and improve the reliability of sensor combination predictions, followed by achieving adaptive multi-sensor decoupling to ensure accurate 3D object detection while reducing inference latency. We implement and evaluate Chameleon using the nuScenes and nuScenes-C datasets. The experimental results show that Chameleon achieves average improvements of 1.10% in the mAP and 0.61% in the NDS compared to the state-of-the-art method across various traffic scenes.

Existing autonomous driving systems are optimized for egocentric efficiency metrics, which are in fundamental conflict with the socialized expectations and habitual patterns of human drivers. This contradiction stems from the traditional approach's dual neglect of the trade-offs between self and other in driving decisions, and the culturally rooted social qualities of traffic interactions. To this end, this paper proposes a dual-adaptation framework that integrates social value orientation (SVO) and human-in-the-Ioop(HITL) guidance, modeling vehicular interactions as competitive-cooperative agents by quantifying the social utility function, and dynamically calibrating the SVO parameters with the help of real-time human feedback. The method innovatively transforms abstract social preferences into mathematically tractable decision boundaries, enabling the human-vehicle co-evolutionary mechanism to contextualize self-adaptation according to the regional driving etiquette, and thus cracking the inherent contradiction between individual trajectory optimization and group traffic harmony. Empirical studies based on Highway Env driving scenarios show that compared with pure reinforcement learning methods, this method reduces human-vehicle interaction conflicts while maintaining self-vehicle efficiency. The research results provide a quantifiable interaction paradigm and a verifiable training architecture for the construction of culturally-aware autonomous driving systems through the deep coupling of computational social value modeling and human social intelligence.

In this study, we introduce an AR-based Meta-Vehicle Road Collaboration Testing System (AR-MVRTs), a significant advancement in autonomous driving testing. This system utilizes vehicle-road collaboration, Augmented Reality (AR), and Metaverse technologies for high-risk scenario simulation and dynamic interaction between virtual data and actual vehicles and infrastructure, enhancing verification and optimization methods. The core contribution is the innovative framework and component model, integrating AR with vehicle-road collaboration technologies for a comprehensive environment that includes real autonomous vehicles, virtual scenarios, and parameterized test settings. The AR-MVRTs method efficiently generates critical testing scenarios, significantly improving testing efficiency. An implementation case shows the system’s practical application, where AR-MVRTs demonstrated a 658-fold efficiency increase, completing tests in 8.5 hours compared to 5580 hours required by traditional test matrices. This integration accelerates the transition from theoretical research to practical applications and offers deep insights into autonomous vehicle safety and reliability. This research promises to advance autonomous driving technology and lay a solid foundation for its future development.

User alignment refers to linking a user's accounts across multiple social networks, which is important for studying community discovery, recommendation systems, and other related fields. However, existing methods primarily perform user alignment by correlating user features, neglecting the causal relationship between network topology and user alignment, which makes it challenging to achieve superior user alignment accuracy and generalization capabilities. Therefore, we propose a counterfactual inference-based social network user-alignment algorithm (CINUA). This improves user connection retention due to the non-Euclidean geometric characterization of hyperbolic spaces. The similarity of aligned users is augmented using a hyperbolic graph attention network. User-feature embedding and fusion facilitate user relevance mining. Furthermore, there are causal relationships between network topology structure and user linkages. In various communities, there are some highly similar user pairs, and based on counterfactual inference, the network topology is adjusted to enhance sample diversity. Multilevel factual and counterfactual networks are constructed through iterative diffusion based on user alignment and their linkages. By integrating the users’ causal features in multiple networks, the accuracy and generalization capabilities of the user alignment model are effectively improved. In this article, the experimental results indicate that CINUA achieves a user alignment accuracy improvement of 5.98% and 3.03%, on two datasets respectively compared to the baseline methods on average. CINUA can achieve favorable alignment results even when the training dataset is small. This demonstrates that our algorithm can ensure both user alignment accuracy and generalization capability.

With the advancement of smart cities and the development of heterogeneous vehicular networks (HetVNets), vehicles can collect data and generate valuable information to obtain profits, thus forming a new vehicular self-media paradigm in HetVNets. However, in the HetVNets with potential security risks, the vehicular self-media market lacks the consideration of the values of the data owned by the media data producers (MDPs) and the capabilities of the media data sellers (MDSs) to improve their utilities. To this end, we propose a value-based secure self-media data trading scheme in the HetVNets. Specifically, we first design a vehicular self-media trading mechanism based on smart contracts to provide participants with a safe and reliable transaction environment. Then, we model the interactions between the MDPs and the MDSs as a Stackelberg game by considering the values of various media data and the sales capabilities of different MDPs. After that, we design an iterative method to obtain the optimal game strategies for the MDPs and the MDSs to maximize their utilities. Compared with the traditional schemes, the simulation results show that our scheme can obtain the optimal strategies for the MDPs and the MDSs and bring them the highest utilities.

The customization of edge computing services is one of the key research fields in sixth-generation (6G) heterogeneous vehicular networks (HetVNETs). With various personalized requirements of vehicles on computation-intensive applications, how to explore the heterogeneous computing resources in the 6G HetVNETs to guarantee vehicles with the customized Quality of Experience (QoE), therefore, becomes a challenge. In this article, we develop a novel secure scheme to provide personalized edge computing services for moving vehicles (MVs) in 6G HetVNETs. In the scheme, a smart-contract-based secure edge computing architecture is designed by jointly considering the attack models and the characteristics of the 6G network infrastructures (e.g., satellites, drones, base stations, and roadside units), where each network infrastructure manages a number of parking vehicles to complete computing services collaboratively. With this architecture, based on the available computing resources owned by different network infrastructures, the collaborative computing resource allocation algorithm is designed to help each network infrastructure decide a customized service strategy (CSS) to satisfy the QoE of MVs. After deciding the CSSs, a model based on the second price-sealed auction is formulated to describe the competition among the network infrastructures, where the Nash equilibrium of the game is obtained to guide their optimal bidding strategies to obtain the chance for completing the services. The security analysis and the simulation results show that the proposed scheme can defend against the attacks and lead to a lower cost for completing the services than the conventional schemes.

The vehicular crowdsensing, which benefits from edge computing devices (ECDs) distributedly selecting autonomous vehicles (AVs) to complete the sensing tasks and collecting the sensing results, represents a practical and promising solution to facilitate the autonomous vehicular networks (AVNs). With frequent data transaction and rewards distribution in the crowdsensing process, how to design an integrated scheme which guarantees the privacy of AVs and enables the ECDs to earn rewards securely while minimizing the task execution cost (TEC) therefore becomes a challenge. To this end, in this article, we develop a blockchain-based collaborative crowdsensing (BCC) scheme to support secure and efficient vehicular crowdsensing in AVNs. In the BCC, by considering the potential attacks in the crowdsensing process, we first develop a secure crowdsensing environment by designing a blockchain-based transaction architecture to deal with privacy and security issues. With the designed architecture, we then propose a coalition game with a transferable reward to motivate AVs to cooperatively execute the crowdsensing tasks by jointly considering the requirements of the tasks and the available sensing resources of AVs. After that, based on the merge and split rules, a coalition formation algorithm is designed to help each ECD select a group of AVs to form the optimal crowdsensing coalition (OCC) with the target of minimizing the TEC. Finally, we evaluate the TEC of the task and the rewards of the ECDs by comparing the proposed scheme with other schemes. The results show that our scheme can lead to a lower TEC for completing crowdsensing tasks and bring higher rewards to ECDs than the conventional schemes.

Although poorly positioned nucleosomes are ubiquitous in the eukaryotic genome, they are difficult to identify with existing nucleosome identification methods. Recently available enhanced high-throughput chromatin conformation capture techniques such as Micro-C, DNase Hi-C, and Hi-CO characterize nucleosome-level chromatin proximity, probing the positions of mono-nucleosomes and the spacing between nucleosome pairs at the same time, enabling nucleosome profiling in poorly positioned regions. Here we develop a novel computational approach, NucleoMap, to identify nucleosome positioning from ultra-high resolution chromatin contact maps. By integrating nucleosome read density, contact distances, and binding preferences, NucleoMap precisely locates nucleosomes in both prokaryotic and eukaryotic genomes and outperforms existing nucleosome identification methods in both precision and recall. We rigorously characterize genome-wide association in eukaryotes between the spatial organization of mono-nucleosomes and their corresponding histone modifications, protein binding activities, and higher-order chromatin functions. We also find evidence of two tetra-nucleosome folding structures in human embryonic stem cells and analyze their association with multiple structural and functional regions. Based on the identified nucleosomes, nucleosome contact maps are constructed, reflecting the inter-nucleosome distances and preserving the contact distance profiles in original contact maps.

International audience