In modern industrial scenarios, federated learning (FL) has been widely adopted due to its advantages in privacy preservation and distributed modeling. However, existing FL approaches rely on a single update paradigm, which significantly hampers communication efficiency. Moreover, the server preserves historical aggregation states to model causal propagation across training rounds, instead of performing memoryless per-round aggregation. For this end, we propose a self-adapting federated continual learning with gradient causal constraint (GCC-SFCL). First, the selection of synchronous or asynchronous communication across FL rounds is reformulated as continual learning task, where an experience accumulation mechanism records historical aggregation states to model long-term temporal dependencies among client updates. Based on the aggregated context, we further design a self-adapting scheduling strategy driven by multidimensional client state information, enabling the server to dynamically select optimal aggregation. Furthermore, a causality-steering gradient constraint is introduced to explicitly regulate gradient updates, improving the interpretability of the causal model and allowing the capture of fault propagation paths. Experimental results of a simulation case and the real-world case demonstrate that GCC-SFCL significantly improves not only communication efficiency but also the accuracy of fault propagation path identification.

Large group decision-making (LGDM) involves multiple decision-makers (DMs) and criteria, frequently resulting in conflicts and inconsistencies. This study proposes a novel conflict resolution model based on dynamic trust relationships to effectively identify and address potential disputes in LGDM. First, hesitant fuzzy 2-tuple linguistic sets (HF2TLSs) are utilized to accurately capture DMs’ preferences in uncertain environments. Then, a Markov trust state transition model is developed to capture the dynamic evolution of trust relationships. Next, an enhanced PageRank algorithm, built on trust networks, is employed to determine the weights of DMs and subgroups, thereby improving decision quality. Additionally, multidimensional conflict detection indicators are introduced to quantify cognitive and interest conflicts among subgroups. Finally, an adaptive conflict resolution mechanism is presented to balance heterogeneous interests and achieve agreement. An illustrative example validates the model, with comparative analyses demonstrating its rationality and superiority.

Recently, the trend of developing digital twins for smart cities has driven a need for managing large-scale multidimensional, dynamic, and scene-oriented spatial data. Due to larger data scale and more complex data structure, queries over such data are more complicated and expensive than those on traditional spatial data, which poses challenges to the system efficiency and deployment costs. The existing spatial databases have limited support in both data types and operations. Therefore, a new-generation spatial database with excellent performance and effective deployment costs is needed. This paper presents Ganos, a cloud-native spatial database engine of PolarDB for PostgreSQL that is developed by Alibaba Cloud, to efficiently manage multidimensional, dynamic, and scene-oriented spatial data. Ganos models 3D space and spatio-temporal dynamics as first-class citizens. Also, it natively supports spatial/spatio-temporal data types such as 3DMesh, Trajectory, Raster, PointCloud, etc. Besides, it implements a novel extended-storage mechanism that utilizes cloud-native object storage to reduce storage costs and enable uniform operations on the data in different storages. To facilitate processing "big" queries, Ganos extends PolarDB and provides spatial-oriented multi-level parallelism under the architecture of decoupling compute from storage in cloud-native databases, which achieves elasticity and excellent query performance. We demonstrate Ganos in real-life case studies. The performance of Ganos is evaluated using real datasets, and promising results are obtained. Finally, based on the extensive deployment and application of Ganos, the lessons learned from our customers and the expectations of modern cloud applications for new spatial database features are discussed.

We introduce PreCo, a large-scale English dataset for coreference resolution.The dataset is designed to embody the core challenges in coreference, such as entity representation, by alleviating the challenge of low overlap between training and test sets and enabling separated analysis of mention detection and mention clustering.To strengthen the training-test overlap, we collect a large corpus of 38K documents and 12.5M words which are mostly from the vocabulary of Englishspeaking preschoolers.Experiments show that with higher training-test overlap, error analysis on PreCo is more efficient than the one on OntoNotes, a popular existing dataset.Furthermore, we annotate singleton mentions making it possible for the first time to quantify the influence that a mention detector makes on coreference resolution performance.