Cross-database knob tuning has long been recognized as a critical but complex task. Modern database systems expose hundreds of configuration knobs that play key roles in memory management, concurrency control, and query optimization. Proper tuning can significantly improve performance, while improper settings can cause severe degradation. Despite progress with black-box methods like reinforcement learning and bayesian optimization, as well as LLM-based tuning guides, challenges remain, such as modeling knob dependencies, high cold-start trial costs, and weak handling of dynamic workloads. We propose KnobTuneX, a structure-aware, LLM-enhanced framework for automatic database tuning that integrates domain knowledge, historical behaviors, and reasoning capabilities to adapt to diverse workloads. The approach features an offline learning stage to capture knob-performance relationships and build a historical RAG store, and an online inference stage that dynamically recommends knobs based on structured reasoning and historical insights. By explicitly modeling dependencies among knobs and leveraging LLMs for informed decision-making, the framework achieves both interpretability and adaptability. Finally, we evaluate KnobTuneX on PostgreSQL and show that the method outperforms mainstream approaches in efficiency, scalability, and overall tuning quality across OLTP and OLAP workloads. The implementation of our work can be found at https://github.com/vjwww/KnobTuneX.

Generating high-quality pseudo-labels on the cloud side is crucial for cloud-edge collaborative object detection, especially in dynamic traffic monitoring scenarios where the target data distribution continuously evolves. Existing methods often assume a perfectly reliable cloud model, neglecting the potential for errors in the cloud’s predictions, or employ simple adaptation techniques that struggle to handle complex distribution shifts. This paper proposes a novel Cloud-Adaptive High-Quality Pseudo-label generation algorithm (CA-HQP) that addresses these limitations by incorporating a learnable Visual Prompt Generator (VPG) and a dual feature alignment strategy into the cloud model updating process. The VPG enables parameter-efficient adaptation of the large pre-trained cloud model by injecting task-specific visual prompts into the model’s input, enhancing its flexibility without extensive fine-tuning. To mitigate domain discrepancies, CA-HQP introduces two complementary feature alignment techniques: a global Domain Query Feature Alignment (DQFA) that captures scene-level distribution shifts and a fine-grained Temporal Instance-Aware Feature Embedding Alignment (TIAFA) that addresses instance-level variations. Extensive experiments on the Bellevue traffic dataset, a challenging real-world traffic monitoring dataset, demonstrate that CA-HQP significantly improves the quality of pseudo-labels compared to existing state-of-the-art cloud-edge collaborative object detection methods. This translates to notable performance gains for the edge model, showcasing the effectiveness of CA-HQP in adapting to dynamic environments. Further ablation studies validate the contribution of each individual component (DQFA, TIAFA, VPG) and confirm the synergistic effect of combining global and instance-level feature alignment strategies. The results highlight the importance of adaptive cloud model updates and sophisticated domain adaptation techniques for achieving robust and accurate object detection in continuously evolving scenarios. The proposed CA-HQP algorithm provides a promising solution for enhancing the performance and reliability of cloud-edge collaborative object detection systems in real-world applications.

Automated test case generation based on path coverage is not only a large-scale black-box optimization problem , but also a key scientific problem in software automatic testing technology. Evolutionary algorithms and other search-based algorithms are representative methods for this problem. However, existing research mainly focuses on the multi-function case, where generating test cases for multiple functions is difficult due to the combinatorial explosion of the path number. In this paper, we propose a multi-task collaborative method based on manifold optimization by considering the topological manifold relationship between the test case space (decision space) and the program path space (target space). This method achieves the goal of collaborative optimization for different function coverage tasks by coordinating the allocation of computing resources and knowledge transfer mechanisms among them. To verify the effectiveness of the proposed method, we compare it with general solution methods for single-function automated test case generation based on path coverage, such as the manifold-inspired search-based algorithm. The experimental results show that the proposed method outperforms the compared single-function optimization algorithms especially on programs with strong coding similarities. This study verifies the feasibility of collaborative optimization methods for solving large-scale black-box optimization problems, such as the automated test case generation based on path coverage, and expands their application scenarios.

MOTIVATION: Many biological processes are regulated by single molecules and molecular assemblies within cells that are visible by microscopy as punctate features, often diffraction limited. Here, we present detecting-NEMO (dNEMO), a computational tool optimized for accurate and rapid measurement of fluorescent puncta in fixed-cell and time-lapse images. RESULTS: The spot detection algorithm uses the à trous wavelet transform, a computationally inexpensive method that is robust to imaging noise. By combining automated with manual spot curation in the user interface, fluorescent puncta can be carefully selected and measured against their local background to extract high-quality single-cell data. Integrated into the workflow are segmentation and spot-inspection tools that enable almost real-time interaction with images without time consuming pre-processing steps. Although the software is agnostic to the type of puncta imaged, we demonstrate dNEMO using smFISH to measure transcript numbers in single cells in addition to the transient formation of IKK/NEMO puncta from time-lapse images of cells exposed to inflammatory stimuli. We conclude that dNEMO is an ideal user interface for rapid and accurate measurement of fluorescent molecular assemblies in biological imaging data. AVAILABILITY AND IMPLEMENTATION: The data and software are freely available online at https://github.com/recleelab. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.