Continual Structured Knowledge Reasoning (CSKR) focuses on training models to handle sequential tasks, where each task involves translating natural language questions into structured queries grounded in structured knowledge. Existing general continual learning approaches face significant challenges when applied to this task, including poor generalization to heterogeneous structured knowledge and inefficient reasoning due to parameter growth as tasks increase. To address these limitations, we propose a novel CSKR framework, \textsc{K-DeCore}, which operates with a fixed number of tunable parameters. Unlike prior methods, \textsc{K-DeCore} introduces a knowledge decoupling mechanism that disentangles the reasoning process into task-specific and task-agnostic stages, effectively bridging the gaps across diverse tasks. Building on this foundation, \textsc{K-DeCore} integrates a dual-perspective memory consolidation mechanism for distinct stages and introduces a structure-guided pseudo-data synthesis strategy to further enhance the model's generalization capabilities. Extensive experiments on four benchmark datasets demonstrate the superiority of \textsc{K-DeCore} over existing continual learning methods across multiple metrics, leveraging various backbone large language models.

This paper presents a mixed far-field (FF) and near-field (NF) source localization method based on Newtonalized orthogonal matching pursuit (NOMP). First, the orthogonal matching pursuit (OMP) algorithm is used to coarsely estimate the angles and ranges of the mixed sources, then Newton refinement is applied to locally refine the previous estimates. In the refinement stage, the 1-D NOMP and 2-D NOMP algorithms are presented to refine the parameters of the FF and NF sources, respectively. The proposed method can achieve off-grid estimation and solve the potential grid-mismatch problem. Unlike most of the existing mixed source localization methods, it can separate and localize the mixed sources with the half-wavelength spacing linear array. The simulation results show that it has higher accuracy compared with several existing methods.

Recently, uncertain knowledge graphs (UKGs), where each relation between entities is associated with a confidence score, have gained much attention. Compared with traditional knowledge graphs, UKGs possess the capability of uncertainty knowledge expression, which facilitates more reliable and precise knowledge graph reasoning by not only completing missing triples but also predicting triple confidences. In this paper, we release unKR, the first open-source python library for uncertain Knowledge graph (UKG) Reasoning by representation learning. We design a unified framework to implement two types of representation learning models for UKG reasoning, i.e., normal and few-shot ones. Besides, we standardize the evaluation tasks and metrics for UKG reasoning to ensure fair comparisons, and report the detailed results of each model under the consistent test setting. With unKR, it is effortless for users to reproduce existing models, as well as efficiently customize their own models. The library, documentation, demo, and re-implementing results are all publicly released at https://github.com/seucoin/unKR.

In this paper, we present a mixed far-field (FF) and near-field (NF) source localization algorithm based on low-rank matrix reconstruction (LRMR). At first, the signal covariance matrix is recovered by solving an optimization problem to obtain the signal component and suppress the noise component via LRMR. Then, according to the characteristics of the covariance matrix of a symmetric uniform linear array, the 2D parameters of range and direction-of-arrival (DOA) are separated, in which the range is eliminated by extracting the inverse diagonal elements of the reconstructed matrix. The extracted elements are reconstructed into a Toeplitz matrix, and the DOAs of the FF and NF sources are estimated. Finally, on the basis of the estimated DOA, the range parameter of the NF source is obtained. The proposed method can distinguish different types of the sources. The simulation results show that it has higher estimation accuracy than the conventional subspace-based methods in relatively low SNR, and can obtain automatic parameter pairing of the DOA and range for multiple mixed sources.

Energy consumption is the main obstacle to the green cloud computing, particularly with the global climate warming and data center scale expanding. The green computing is gaining more and more attention because energy consumption increased rapidly. We propose a cloud computing management framework in order to ensure energy consumption of cloud computing to be a minimum. The framework uses virtual date center instead of virtual machine service mode of traditional service providers, and partition the virtual data center used in the management framework is based on the network communications of virtual machines. Then the virtual data center partition is placed into corresponding green data center in order to maximize revenue of providers and minimize the carbon emissions.