Substructure mining serves as a fundamental technique for uncovering key patterns and latent risk within complex networks.Existing methods focus on modeling of topological and semantic information, while neglecting the cross-layer semantic confusion and initial decisions unreliability resulting from intrinsic coupling between them.Faced with that, this study proposes a novel Graph Transformer model based on fact reflection, named RAGphormer, which enhances substructure mining through high-order semantic modeling and dynamic decision reflection.Specifically, we introduce the Sub2Token module, which constructs hierarchical semantic encoding sequences by integrating node-level, substructure-level, and multi-hop neighborhood features.It overcomes the limitations of cross-layer semantic sharing among multi-hop neighborhood nodes, and mitigates the noise caused by multi-hop semantic entanglement.Furthermore, a dynamic decision reflection mechanism is proposed, which constructs substructure fact and counterfactual feature patterns based on an adaptive KMeans clustering algorithm.A dynamic thresholding strategy is employed to identify potentially unreliable predictions, which are then revised based on the discrepancies between corresponding feature patterns.Extensive experiments on two private datasets (ChTech and EnTech) and one public dataset (Cora) demonstrate that RAGphormer outperforms baseline methods across multiple evaluation metrics, achieving an accuracy of up to 98.8%.

A wideband high dynamic range software radio receiver for sub-6-GHz applications is proposed. The receiver combines three gain variable modules and an automatic gain control module to achieve high gain dynamic range and constant output voltage. A broadband resistive feedback LNA was designed, and two novel gain switching methods were proposed for the resistive feedback LNA, which reduced the LNA gain while optimizing the input matching performance. In addition, the receiver adopts a voltage mode mixer and uses the impedance translation property to achieve blocker-tolerant performance. In analog baseband (ABB), a fourth-order Butterworth active-RC low-pass filter (LPF) is employed for the purpose of attenuating interference. Furthermore, the receiver is furnished with an automatic gain control amplifier to uphold a consistent output voltage amplitude. A 22 nm CMOS receiver prototype occupies 1.85 mm2 and consumes 51 mW from a 1 V power supply over the 2–6 GHz operating frequency range. The receiver achieves a gain dynamic range of 9.1-91.9 dB and a noise figure (NF) of 2.4-3.4 dB. When a 0 dBm blocker with a frequency offset of 80 MHz is injected into the receiver, the NF increases to 13.2 dB. The programmable bandwidth of the LPF can be adjusted from 10 to 160 MHz. Moreover, the receiver achieved an in-band third-order input-referred intercept point (IIP3) of −9.1 dBm and an out-of-band IIP3 of −2.9 dBm respectively.

In the contemporary social environment, social crisis events occur frequently with significant impacts. Effective management of these events requires comprehensive group intention mining, which encompasses intention detection and intention attribution. Knowledge graph inference facilitates the detection of group intention in crisis events. This is supported by the construction of crisis knowledge graphs, which organize crisis elements and inter-element relations into structured semantic information. This paper provides a comprehensive overview of the research about knowledge graph in social crisis management, focusing on three key areas: knowledge graph construction and inference, knowledge graph-based interpretable crisis attribution, and risk management. Specifically, the interpretable semantics in crisis knowledge graphs enables attribution of intention. To illustrate the significance of knowledge graphs in group intention mining, the COVID-19 and China–US game events are selected as two case studies. Finally, the paper proposes future research directions to solve the limitations of existing knowledge graph-related methods in social crises.

While significant progress has been made in recognizing entities from plain text, the exploration of entity recognition from multimodal data remains limited due to disparities in semantic representation. In light of this challenge, given the supportive nature of visual and text data, we propose a novel entity recognition model called Heterogeneous Graph Reasoning(HGR), leveraging the synergistic nature of visual and textual data. This is achieved through the utilization of the Vision Refine and Graph Cross Inference modules. In the Vision Refine module, semantically relevant objects hidden in the image are selected to aid in the text entity extraction. In the Graph Cross Inference module, cross-association inference between visual regions and textual entities is constructed through graph construction, heterogeneous graph fusion, visual region refinement and cross inference. Extensive experiments on four multimodal datasets are demonstrate the superiority of our model, when compared to the second-best state-of-the-art model.

In the current social environment, social crisis events occur frequently with significant impacts.Group intention mining through automatic technologies for managing social crises has gained extensive attention.This paper presents an overview of research on group intention mining in social crisis events, covering three areas: knowledge graph inference, intention attribution, and risk management.Knowledge graph inference facilitates the detection of group intention in crisis events.It is supported by the construction of crisis knowledge graphs, which organize crisis elements and inter-element relations into structured semantic knowledge.The interpretable semantics in the crisis knowledge graphs enables attribution of intention.Group intention mining consists of intention detection and intention attribution, serving the risk management of social crisis events.To gain insights into the process of group intention mining in social crises, the Covid-19 event is selected as a case study.Finally, the paper proposes future research directions to solve the limitations of existing intention mining methods in social crises.

This article describes a wideband low-noise frequency synthesizer implemented in 0.13-μm SiGe BiCMOS process for 5G millimeter-wave applications. To extend the frequency range while reducing the phase noise, a fundamental voltage-controlled oscillator (VCO) array including four Colpitts VCO cores with switchable bias circuits is adopted in the proposed frequency synthesizer. A ring-oscillator-based injection locked frequency divider is utilized as the wideband divideby-2 prescaler, and its bandwidth is optimized based on a new injection-locked behavior model. This fabricated frequency synthesizer can be locked in a range from 23 to 36.8 GHz (46.2%) by a 100-MHz step. It achieves a phase noise of -94.7 dBc/Hz at the 1-MHz offset and an output power of -3.5 dBm measured at 36.8 GHz. The chip consumes 360.6 mW from 3.3 and 1.8 V supplies and has an area of 2.7 × 3.1 mm2.