Event-Based cameras offer significant advantages due to their high temporal resolution and low power consumption. However, when deploying multiple such cameras, a critical challenge emerges: each camera operates on an independent time system, resulting in temporal misalignment that severely degrades performance in multi-event camera applications. Traditional hardware-based synchronization methods face significant limitations in compatibility and are impractical for wide-baseline configurations. We introduce EventSync, a software-based algorithm that achieves millisecond-level synchronization by exploiting the motion of objects in the cameras’ shared field of view, while simultaneously estimating the relative orientation between cameras. Our approach eliminates the need for physical connections, making it particularly valuable for wide-baseline deployments. Through comprehensive evaluation in both simulated environments and real-world indoor/outdoor scenarios, we demonstrate robust synchronization accuracy and precise extrinsic calibration across varying camera configurations, significantly outperforming existing methods. Code: https://github.com/wlxing1901/event-sync

Homomorphically encrypted matrix operations are extensively used in various privacy-preserving applications. Consequently, reducing the cost of encrypted matrix operations is a crucial topic on which numerous studies have been conducted. In this paper, we introduce a novel matrix encoding method, named bicyclic encoding, under which we propose two new algorithms$\textsf {BMM}\text {-}\textsf {I}$and$\textsf {BMM}\text {-}\textsf {II}$for encrypted matrix multiplication.$\textsf {BMM}\text {-}\textsf {II}$outperforms the stat-of-the-art algorithms in theory, while$\textsf {BMM}\text {-}\textsf {I}$, combined with the segmented strategy, performs well in practice, particularly for matrices with high dimensions. Another noteworthy advantage of bicyclic encoding is that it allows for transposing an encrypted matrix entirely free. A comprehensive experimental study based on our proof-of-concept implementation shows that each algorithm introduced in this paper has specific scenarios outperforming existing algorithms, achieving speedups ranging from 2x to 38x.