Adaptive learning, which requires the in-depth understanding of students' learning processes and rational planning of learning resources, plays a crucial role in intelligent education. However, how to effectively model these two processes and seamlessly integrate them poses significant implementation challenges for adaptive learning. As core learning resources, exercises have the potential to diagnose students' knowledge states during the learning processes and provide personalized learning recommendations to strengthen students' knowledge, thereby serving as a bridge to boost student-oriented adaptive learning. Therefore, we introduce a novel task called Knowledge-aware Exercise Generative Recommendation (KEGR). It aims to dynamically infer students' knowledge states from their past exercise responses and customizably generate new exercises. To achieve KEGR, we propose an adaptive multi-agent cooperation framework, called ExeGen, inspired by the excellent reasoning and generative capabilities of LLM-based AI agents. Specifically, ExeGen coordinates four specialized agents for supervision, knowledge state perception, exercise generation, and quality refinement through an adaptive loop workflow pipeline. More importantly, we devise two enhancement mechanisms in ExeGen: 1) A human-simulated knowledge perception mechanism mimics students' cognitive processes and generates interpretable knowledge state descriptions via demonstration-based In-Context Learning (ICL). In this mechanism, a dual-matching strategy is further designed to retrieve highly relevant demonstrations for reliable ICL reasoning. 2) An exercise generation-adversarial mechanism collaboratively refines exercise generation leveraging a group of quality evaluation expert agents via iterative adversarial feedback. Finally, a comprehensive evaluation protocol is carefully designed to assess ExeGen. Extensive experiments on real-world educational datasets and a practical deployment in college education demonstrate the effectiveness and superiority of ExeGen. The code is available at https://github.com/dsz532/exeGen.

Accurate prediction of sea surface temperature (SST) plays a critical role in climate research and marine ecosystem management. Traditional models predict trends by analyzing and fitting data, but they struggle with capturing long-range dependencies and complex spatiotemporal patterns. The transformer’s attention mechanism effectively addresses long-range dependencies, but its high computational complexity poses challenges. To overcome these limitations, this study proposes a novel spatiotemporal sequence prediction model: the spatiotemporal vision mamba recurrent neural network (SVRNN). The model innovatively integrates a bidirectional state-space processing mechanism and decoupled memory modules. The bidirectional mechanism maintains a global receptive field with linear computational complexity, while the decoupled memory modules explicitly separate spatiotemporal dependencies, enhancing the model’s ability to capture complex spatiotemporal patterns. During the experiment on hourly SST prediction in the Taiwan Strait, where the SST of the next 12 h was predicted using data from the previous 12 h, the SVRNN model demonstrated superior performance, achieving a root mean square error (RMSE) of$0.159~^{\circ }$C, a mean absolute error (MAE) of$0.105~^{\circ }$C, and a mean absolute percentage error (MAPE) of 0.496%. Furthermore, our seasonal error analysis reveals that the model exhibits robust performance in different seasons, providing more reliable technical support for SST prediction in Taiwan Strait.

Modern data-centric applications frequently need to store and read data with low latency. These requirements are difficult to achieve, even on high performance processors paired with fast solid state drives (SSDs). To this end, LSM tree is widely used in many systems such as in RocksDB and considered as an ideal index structure that fits SSDs. However, in spite of many improvements to LSM tree over the years, fundamental problems of limited read performance and expensive compaction operations remain. Microsoft Research proposed Bw-tree, a variant of B+ tree layered on top of log structured storage. Bw-tree achieves fast ingestion of data, similar to LSM tree, meanwhile it has less drawback on read performance and compaction. However, except for Microsoft, the industrial strength implementation of Bw-tree is rare. The open source OpenBw-Tree from Carnegie Mellon University was designed only for main memory. This paper describes Bwe-tree, an implementation and a significant evolution of Bw-tree on fast storage. It makes two contributions. First, Bwe-tree addresses reliability and performance issues revealed during running Bw-tree on fast storage in production, by revising structural modification operations, introducing page concurrency control, and storing large-size values off-tree. Performance improvements over Bw-tree are verified by experiments. Second, it demonstrates that Bw-tree is an effective alternative tree structure on SSDs. Compared to RocksDB (LSM tree) and BerkeleyDB (B+ tree), Bwe-vtree performs dramatically better (up to 3X or more) for the YCSB workloads. Our Bwe-vtree implementation has been integrated into production systems in Alibaba, including a flagshin cloud-native database service.

Phase-sensitive optical time-domain reflectometry (Φ-OTDR), which utilizes the phase information of Rayleigh scattered lightwave inside optical fiber, could turn a fiber cable into a massive sensor array for distributed acoustic sensing (DAS), i.e., an emerging infrastructure for Internet of Things. Given a certain fiber length, there are tradeoffs among the sensing bandwidth, the sensitivity, and the spatial resolution. In this paper, the concept of linearization and Golay pulse-coding for heterodyne Φ-OTDR are proposed and experimentally verified for the first time. First, we gave a full theoretical treatment on how an intensity-coded yet phase-retrieved Φ-OTDR can be built up as a fully linear system, therefore a significant enhancement of signal-to-noise ratio becomes viable and the sensing bandwidth equals the four-times averaging case. Second in the proof-of-concept experiment, submeter gauge length and nanostrain resolution were realized with 10 km sensing range, in other words, more than ten thousand sensitive sensing units were realized along the fiber. This paper makes a significant step toward high-performance DAS with orders-of-magnitude performance enhancement.