Pose estimation from unordered images is fundamental for 3D reconstruction, robotics, and scientific imaging. Recent geometric foundation models, such as DUSt3R, enable end-to-end dense 3D reconstruction but remain underexplored in scientific imaging fields like cryo-electron microscopy (cryo-EM) for near-atomic protein reconstruction. In cryo-EM, pose estimation and 3D reconstruction from unordered particle images still depend on time-consuming iterative optimization, primarily due to challenges such as low signal-to-noise ratios (SNR) and distortions from the contrast transfer function (CTF). We introduce CryoFastAR, the first geometric foundation model that can directly predict poses from Cryo-EM noisy images for Fast ab initio Reconstruction. By integrating multi-view features and training on large-scale simulated cryo-EM data with realistic noise and CTF modulations, CryoFastAR enhances pose estimation accuracy and generalization. To enhance training stability, we propose a progressive training strategy that first allows the model to extract essential features under simpler conditions before gradually increasing difficulty to improve robustness. Experiments show that CryoFastAR achieves comparable quality while significantly accelerating inference over traditional iterative approaches on both synthetic and real datasets.

Blockchain sharding is a promising solution for scalability but struggles to reach the expected performance due to the high ratio of cross-shard transactions. Account migration has emerged as a critical approach to optimizing shard performance. However, existing migration solutions suffer from inefficient handling of queued withdrawal transactions from a migrating account and inadequate priority mechanism for migration transaction, resulting in prolonged transaction makespan and reduced system throughput. This paper proposes Caravan, a novel blockchain sharding system for optimizing account migration. First, Caravan proposes a transaction aggregation-based migration scheme to efficiently handle withdrawal congestion post-migration. It incorporates a multi-level Merkle tree and cross-shard synchronization protocol to ensure cross-shard security. Second, Caravan presents an economic incentive-driven priority mechanism that motivates miners to perform transaction aggregation and prioritize migration transactions by increasing the associated revenue. Furthermore, its gas recycling strategy enables users to finance migration costs without awareness or extra expenses. Finally, we develop the Caravan prototype, deploy it on Alibaba Cloud, and experiment with real Ethereum transactions. The results show that compared to the state-of-the-art account migration schemes, Caravan significantly mitigates the transaction surge caused by migration, achieving up to a 3.2× throughput improvement and a 65% reduction in transaction confirmation latency. And users share considerable migration costs without extra expenses, significantly reduce system costs. The code for Caravan is available on GitHub.11Caravan are available athttps://github.com/Caravan-project/Caravan.