Enhanced Vehicle-to-Everything (V2X) services require ultra-low latency and robust reliability in end-to-end (E2E) data delivery from producers (e.g., vehicles and roadside units) to consumers (e.g., in-car V2X service applications). One of the candidate data delivery architectures that can meet the stringent requirements is restricting the data delivery path within the network edge by letting an edge server relay data between sources and destinations in its coverage. To that end, edge servers need to equip two important functionalities: 1) a reliable and lightweight data transport protocol to collect data from numerous data sources, and 2) an efficient data streaming platform that delivers collected data to consumers with high throughput and low latency. In this work, we design and implement Kafka- Thor, a Kafka-based high-performance data streaming platform, specifically designed for edge servers to collect and deliver timecritical data for enhanced V2X services. The platform reduces E2E data delivery latency by optimizing the data delivery architecture in Kafka using two novel technologies, the singlepoller, multi-worker (SPMW) architecture and service-specific dynamic batching (SS-batching). Experimental results show that Kafka-Thor significantly improves latency and reliability in E2E data delivery, making enhanced V2X services feasible.

Cell instance segmentation (CIS) is crucial for identifying individual cell morphologies in histopathological images, providing valuable insights for biological and medical research. While unsupervised CIS (UCIS) models aim to reduce the heavy reliance on labor-intensive image annotations, they fail to accurately capture cell boundaries, causing missed detections and poor performance. Recognizing the absence of error-free instances as a key limitation, we present COIN (COnfidence score-guided INstance distillation), a novel annotation-free framework with three key steps: (1) Increasing the sensitivity for the presence of error-free instances via unsupervised semantic segmentation with optimal transport, leveraging its ability to discriminate spatially minor instances, (2) Instance-level confidence scoring to measure the consistency between model prediction and refined mask and identify highly confident instances, offering an alternative to ground truth annotations, and (3) Progressive expansion of confidence with recursive self-distillation. Extensive experiments across six datasets show COIN outperforming existing UCIS methods, even surpassing semi- and weakly-supervised approaches across all metrics on the MoNuSeg and TNBC datasets. The code is available at https://github.com/shjo-april/COIN.