Deep-learning methods have been widely used as an end-to-end modeling strategy of electrical energy systems because of their convenience and powerful pattern recognition capability. However, due to the “closed-box” nature, deep-learning methods have long been blamed for their poor interpretability when modeling a physical system. In this article, we introduce a novel neural network structure, Kolmogorov–Arnold network (KAN), to achieve “glass-box” modeling for electrical energy systems to enhance the interpretability. The most distinct feature of KAN lies in the learnable activation function together with the sparse training and symbolification process. Consequently, KAN can express the physical process with concise and explicit mathematical formulae while retaining the nonlinear-fitting capability of deep neural networks. Simulation results based on three electrical energy systems demonstrate the effectiveness of KAN in the aspects of interpretability, accuracy, robustness, and generalization ability.

Database knob tuning is a long-standing challenge in the database community, aimed at enhancing the performance of database management systems (DBMSs) by minimizing latency and maximizing throughput. Manual tuning, which relies heavily on human expertise, is often inefficient and impractical for large-scale or dynamic deployments. Recent work has explored automating this process using machine learning (ML) and large language models (LLMs). However, existing methods typically require hundreds of workload replays or rely on extensive training data, leading to low tuning efficiency or high preparation costs. Moreover, they also risk generating invalid configurations that can degrade performance or even crash the database. To address these limitations, we introduce AgentTune, the first agent-based knob tuning framework powered by LLMs, designed for efficiency, adaptability, and reliability. AgentTune decomposes the tuning process into four specialized agents: Workload Analyzer, Knob Selector, Range Pruner, and Configuration Recommender, each responsible for a distinct subtask. These agents collaborate through structured prompt chaining. AgentTune first analyzes the input workload to identify impactful knobs and reconstructs their valid ranges to reduce the search space. It then employs a tree-based search strategy to efficiently explore the configuration space and identify suitable knob values. We conduct extensive evaluations across diverse workloads (public benchmarks and real-world workloads), metrics (latency and throughput), DBMSs (PostgreSQL, MySQL, and TiDB), hardware environments, and database scales. Experimental results demonstrate that, compared to existing baselines, AgentTune is able to identify superior configurations using significantly fewer workload replays. Furthermore, AgentTune rarely generates invalid configurations during the tuning process, ensuring reliability and suitability for real-world deployments.

Learned indexes, emerging as a promising alternative to traditional indexes like B+Tree, utilize machine learning models to enhance query performance and reduce memory usage. However, the widespread adoption of learned indexes is limited by their expensive training cost and the need for high accuracy of internal models. Although some studies attempt to optimize the building process of these learned indexes, existing methods are restrictive in scope and applicability. They are usually tailored to specific index types and heavily rely on pre-trained model knowledge, making deployment a challenging task. In this work, we introduce the Learned Index Optimization Framework (LIOF), a general and easily integrated solution aimed at expediting the training process and improving the accuracy of index model for one-dimensional and multi-dimensional learned indexes. The optimization of LIOF for the learned indexes is intuitive, directly providing optimized parameters for index models based on the distribution of node data. By leveraging the correlation between key distribution and node model parameters, LIOF significantly reduces the training epochs required for each node model. Initially, we introduce an optimization strategy inspired by optimization-based meta-learning to train the LIOF to generate optimized initial parameters for index node models. Subsequently, we present a data-driven encoder and a parameter-centric decoder network, which adaptively translate key distribution into a latent variable representation and decode it into optimized node model initialization. Additionally, to further utilize characteristics of key distribution, we propose a monotonic regularizer and focal loss, guiding LIOF training towards efficiency and precision. Through extensive experimentation on real-world and synthetic datasets, we demonstrate that LIOF provides substantial enhancements in both training efficiency and the predictive accuracy for learned indexes.

In the residential demand response area, currently the incentive-based method (e.g., direct load control, DLC) may impair users’ comfort and autonomy, while the price-based method can hardly guarantee users’ engagements. This paper proposes an edge-cloud integrated demand response framework to achieve an effect-predictable residential demand response without harming users’ benefits. First, we combine the cloud-computing resource (cloud) and the home-installed smart thermostats (edges) to formulate an efficient, cost-effective, and data-secured infrastructure to implement the demand response program. Then, we model the demand response problem between the load aggregator and its served residential users as a bi-level optimization problem, and the key is for the load aggregator to find the optimal incentive strategy. To solve this problem, we introduce an RL algorithm, i.e., Continuous Action Reinforcement Learning Automata, to quickly obtain the optimal incentive strategy under an incomplete information scenario. Simulation results based on 136 real-world residential users in Austin area demonstrate that the proposed CEI-DR framework can increase the social welfare by about $8.6/h compared to the traditional DLC method during a normal DR event.