Automatic behavioral analysis of Drosophila has drawn consistent research interest in the field, as understanding quantitative behavior plays a crucial role in neuroscience, genetics, and space biology. Existing machine learning approaches often rely heavily on expert domain knowledge and manual annotations to build supervised models, limiting their scalability and adaptability (e.g., to novel actions and unseen domains). Meanwhile, recent advances in vision-language models offer a more flexible, expressive, and interpretable medium for behavior representation, enabling broader semantic understanding and generalization. To this end, we propose ZS-Drosophila, the first framework that introduces language-guided multimodal alignment for Drosophila behavior analysis. Our foundation model ZS-Drosophila learns transferable behavior representations that can generalize to unseen behaviors and domains. Furthermore, we construct SpaceAnimal-Drosophila, a benchmark dataset of Drosophila video recordings collected both on Earth and in space, comprising annotated skeletal sequences and behaviorlanguage pairs as ground truths. It has a series of evaluation protocols to showcase the strong transferability of ZS-Drosophila, achieved with only prompt tuning at test time. We demonstrate the capabilities of our method in the following scenarios: (1) conventional supervised action recognition on on-Earth data, (2) zero-shot recognition of unseen behaviors, and (3) crossdomain generalization to in-orbit microgravity data. Notably, our proposed model enables zero-shot recognition of novel behaviors potentially induced by microgravity without requiring additional annotations, which, to our knowledge, is the first attempt in the field.

Serverless workflow applications, composed of multiple serverless functions, are increasingly popular in production. However, inter-function communication and cold start latency remain key performance bottlenecks. This paper introduces AlloyStack, a library operating system (LibOS) tailored for serverless workflows. AlloyStack addresses two major challenges: (1) reducing cold start latency through on-demand OS component loading and (2) minimizing data transfer overhead by enabling functions within the same workflow to share a single address space, eliminating unnecessary data copying. To ensure secure isolation, AlloyStack uses Memory Protection Keys (MPK) to separate user functions from the LibOS while maintaining efficient data sharing. Our evaluation shows that AlloyStack reduces cold start times by 98.5% to just 1.3ms. Compared to SOTA systems, AlloyStack achieves a 7.3× to 38.7× speedup in Rust end-to-end latency and a 4.8× to 78.3× speedup in other languages for intermediate data-intensive workflows.

Satellite video intelligent processing plays a critical role in Earth observation applications such as traffic monitoring and environmental surveillance. However, moving-object segmentation in satellite videos faces several challenges. First, spatiotemporal redundancy makes it difficult to model long-range dependencies because large-scale scenes with slow background changes lead to fragmented segmentation. Second, semantic ambiguity arises when stationary objects like parked aircraft share category-level similarities with moving targets, which causes false positives. Besides, insufficient feature discrimination occurs as small, rigid objects such as ships exhibit weak texture and edge details under low-resolution imaging. To overcome these issues, we introduce a semantic affinity-driven spatiotemporal Transformer network that leverages a Transformer-based architecture to capture pixel-level dependencies across spatial and temporal dimensions. Furthermore, our network employs a contextual affinity-constrained decoder to suppress category-level interference and integrates a triple-branch feature extractor with edge priors for enhanced contour delineation. Our framework operates in an end-to-end manner without requiring fine-tuning during inference, which ensures deployment efficiency. Extensive experiments on a dataset built upon SAT-MTB demonstrate state-of-the-art performance with a J&F Mean of 71.7%. The proposed method outperforms the baseline by 3.7% with improvements of 4.4% in J-Mean and 3.1% in F-Mean. In addition, it surpasses the optimized SAM2 with a 10.7% higher J-Mean while maintaining a significantly smaller parameter count (34.6 M versus 224 M). Both qualitative and quantitative evaluations confirm the method’s superiority and temporal stability. This work offers a robust and efficient solution for accurate moving-object segmentation in satellite videos.

Serverless computing enables a new way of building and scaling cloud applications by allowing developers to write fine-grained functions. The execution duration of a cloud function is typically short, usually ranging from a few milliseconds to a few seconds. FaaS providers charge users based on the execution duration of cloud functions with a granularity of 1 millisecond. Existing mixed scheduling methods collocate functions with varying execution times, which may prolong their execution duration and lead to unfair charges for FaaS users. To address this problem, we propose a partition scheduling approach, placing functions with varying execution times on different partitions. We introduce Synergy, a solution in serverless computing that leverages a collaboration of central and local scheduling to partition functions and employs suitable scheduling algorithms for these partitions respectively. Synergy also supports the dynamic switching of scheduling algorithms for partitioned nodes to adapt to highly fluctuating loads. We evaluate Synergy using real-world, representative benchmarks. Experimental results demonstrate that, compared to state-of-the-art and conventional approaches, Synergy can reduce the average function execution duration by $63 \%$.

Serverless platforms use sandbox technology to provide isolated environments for workloads, preventing security attacks from untrusted workloads in multi-tenant environments. Among existing sandbox mechanisms, those based on memory partitioning have the advantage of lightweight isolation, enabling efficient instance scheduling and switch operations, and good scalability. The CAP-VM system, which combines CHERI memory partitioning primitives and library operating system mechanisms, provides good runtime performance and security isolation levels. However, this sandbox system still has an issue of high startup latency, which cannot meet the service quality requirements in Serverless scenarios. To solve the problems existing in the CAP-VM sandbox system, we propose a new snapshot mechanism and sandbox fork caching mechanism. Through this snapshot mechanism, the sandbox system can lower the cold start latency of the sandbox by loading the pre-initialized instance state at startup, and reduce the memory overhead of the sandbox by sharing the instance state. The fork caching mechanism optimizes the sandbox creation process by introducing CAP-VM sandbox fork primitives, reducing the sandbox cold start latency. Compared with the existing CAP-VM sandbox system, the sandbox snapshot system and fork caching mechanism can reduce the sandbox cold start latency by more than ${6 0 \%}$ and ${9 3 . 3 \%}$ respectively.