As vehicular ad hoc networks (VANETs) increase in size and complexity, ensuring secure, flexible, and privacy-preserving vehicle-to-infrastructure (V2I) authentication remains a major challenge. Existing protocols often focus solely on identity verification, overlooking the need for access control based on vehicle attributes. Furthermore, vehicles must obtain authentication credentials from various trusted entities, including automakers, regulators, and government agencies. However, the absence of a unified credential issuance mechanism introduces fragmentation and inconsistencies during the registration process. To address these issues, we propose a V2I authentication protocol, called PriV2I, that integrates distributed credential issuance, attribute-based access control, and strong anonymity guarantees. During vehicle registration, our approach uses Shamir’s Secret Sharing with a threshold t of n across multiple certification authorities (CAs) to consolidate credentials. A vehicle credential can only be issued by a predefined threshold number of CAs, enhancing security and flexibility. Within the authentication protocol, Pointcheval-Sanders (PS) signatures enable fine-grained access control based on vehicle attributes such as type and role. Meanwhile, noninteractive zero-knowledge proofs protect identity privacy by allowing vehicles to prove credential possession and policy compliance without revealing sensitive information. The proposed scheme also supports batch authentication at Roadside Units (RSUs) to efficiently handle high-density environments and includes a comprehensive revocation mechanism to trace and revoke malicious vehicles promptly and securely. In our implementation, the computation cost during the authentication phase is 75.58 ms. The communication overhead per authentication exchange is 992 bytes across two messages. Overall, the protocol provides a secure, scalable, and privacy-preserving solution tailored to modern VANET environments.

In-Context Learning (ICL) has become an effective paradigm for biomedical named entity recognition (BioNER), allowing large language models to recognize entities using only a few examples without extensive fine-tuning. However, ICL's effectiveness depends heavily on selecting appropriate examples, and current approaches that rely primarily on sentence-level semantic similarity often miss the fine-grained entity distinctions and domain-specific characteristics crucial for BioNER tasks. To address these limitations, we propose Entity-aware In-Context Learning (EICL), a framework that improves example selection by incorporating both entity-centric information and domainspecific terminology overlap. EICL employs an Entity Expansion Module that uses LLM capabilities to create explicit entity representations for better structural alignment, along with a Domain Vocabulary Overlap Retrieval Module that measures domain compatibility through terminological analysis. These components are integrated within a hybrid retrieval strategy that combines semantic similarity with terminology overlap scores for more accurate example selection. Experiments across multiple BioNER benchmark datasets show that EICL outperforms existing ICL example selection methods, demonstrating improved generalization and practical effectiveness for biomedical applications.

Heterogeneous surfaces exhibit spatially varying geometry and material, and therefore admit diverse appearances. Existing computer graphics works can only model heterogeneity using explicit structures or statistical parameters that describe a coarser level of detail. We extend the boundary by introducing a new model that describes the heterogeneous surfaces fully statistically at the microscopic level, with rich geometry and material details that are comparable to the wavelengths of light. We treat the heterogeneous surfaces as a mixture of stochastic vector processes. We adapt the well-known generalized Harvey-Shack theory to quantify the mean scattered intensity, i.e., the BRDF of these surfaces. We further explore the covariance statistic of the scattered field and derive its rank-1 decomposition. This leads to a practical algorithm that samples the speckles (fluctuating intensities) from the statistics, enriching the appearance without explicit definition of heterogeneous surfaces. The formulations are analytic, and we validate the quantities by comprehensive numerical simulations. Our heterogeneous surface model demonstrates various applications including corrosion (natural), particle deposition (man-made), and height-correlated mixture (artistic). Code for this paper is available at https://github.com/Rendering-at-ZJU/HeteroSurface.

Abstract The real world consists of mass phenomena, such as iridescence on thin film and metal oxide layers, that is only explicable by wave optics. Existing research can reproduce such effects with simple point lights or low‐frequency environmental lighting. However, it remains a difficult task to efficiently rendering these effects when near‐field, high‐frequency area lights are involved. This paper presents a high‐fidelity, real‐time rendering algorithm for the iridescence effect under polygonal lights. We introduce a novel set of spherical functions, Monomial‐Gaussians, to accurately fit iridescent materials' reflectance. With a precomputed lookup table, the Monomial‐Gaussians are easily integrated over spherical polygons in linear time. Importance sampling of Monomial‐Gaussians is also supported to efficiently reduce Monte‐Carlo error. Our approach produces accurate renderings of the iridescence effect while still preserving high frame rates.