ABSTRACT Co‐speech gestures generation plays a key role in the field of virtual reality interaction, synthesizing proper digital human's actions passively with speech. The generative algorithm‐based method creates realistic gestures accompanying speech's rhythm and semantic content, improving the interactive experience. To match the persona of digital humans, gestures from algorithms often require additional modification before being applied to a virtual character. However, motion sequences are difficult to edit when generated from hidden motion representations. To make motion synthesis editable, the proposed method develops a two‐stage controllable gesture generation pipeline for the c‐speech ge‐ture generating problem. In stage 1, we design a novel large language model based ‐K‐Decoder th‐t takes speech and style label as input to synthesize inverse ki‐ematic s‐yle control points, which are highly editable. In stage 2, we divide the motion sequence into the body or fingers part for VQ‐based latent motion representation learning relatively. And a diffusion‐based IK‐Denoiser is proposed for'latent motion representation synthesis under the condition of control points. Compared to other representative algorithms, the proposed method gets a competitive performance of metrics such as Fréchet Gesture Distance, Beat Consistency, and Diversity. To demonstrate controllability, it provides three explicit control strategies for motion editing. With these control points, we provide a new co‐speech gesture generation paradigm.

Existing magnetic anomaly detection (MAD) methods are widely categorized into target-, noise-, and machine learning-based methods. This article first analyzes the commonalities and characteristics of these methods, unifying them into noise- and target-based frameworks. Focusing on the MAD problem under static sensing systems, and considering that the noise-based methods have better stability in real-world detection but suffer from poor performance at low signal-to-noise ratios (SNRs), this article proposes a novel MAD method based on deep support vector data description (Deep SVDD). The proposed method characterizes long-term magnetic background noise patterns in the region of interest. A deep neural network encoder is employed to extract time–frequency features of the signals, yielding a compact low-dimensional latent representation. The latent space is constrained by a prior distribution derived from a pretrained model, and the probability of noise signal features is maximized in the form of maximum likelihood estimation. To effectively avoid overfitting caused by hypersphere collapse, the Kullback–Leibler (KL) divergence is incorporated into the loss function. Statistical tests and visualizations confirm the method’s effectiveness and alignment with theoretical foundations, while comparative experiments demonstrate that the proposed method achieves significant performance improvements, especially at low SNRs over existing noise-based methods. Furthermore, to prevent the performance collapse in simulation-to-reality transfer that occurs in deep learning (DL) methods driven by semi-realistic data due to inaccurate prior information, this article also proposes a novel semi-supervised learning MAD method driven by sparse prior information about the magnetic anomaly. Experiments demonstrate that the proposed method exhibits superior stability, particularly showing enhanced robustness against$1/f^{\alpha }$noise compared to supervised learning methods. Moreover, the controlled prior information integration mode enables the proposed method to achieve effective tradeoffs between sensitivity and stability in practical deployments. This confirms the method’s reduced dependence on simulation-derived prior information, making it particularly suitable for complex real-world detection scenarios. Finally, the method’s practical performance has been validated using both terrestrial and marine field data.

This brief presents an on-chip, real-time rotation calibration (RRC) technique aimed at alleviating the inter-channel offset mismatch in time-interleaved (TI) successive-approximation register analog-to-digital converter (SAR ADC). By leveraging auto-rotation calibration and self-compensation strategies in the analog domain, the proposed technique demonstrates robust performance across PVT variations. Two additional sub-channels are involved in the TI quantization mechanism, where the continuous rotation of the sampling clock distribution ensures their operation in calibration mode. To validate the effectiveness of the proposed calibration, an$8\times 8$bit 8 GS/s TI-SAR ADC is designed and implemented in a 28-nm process and occupies an active area of 0.273 mm2, with each sub-channel SAR ADC covering only$86\times 23~\mu $m. Extensive simulation results validate the efficacy of RRC, demonstrating significant improvements in dynamic performance. Specifically, SNDR increases from 37.1 to 45.4 dB, while SFDR rises from 57.8 to 60.7 dB, as observed at the Nyquist input frequency.

Employees’ system exploration behaviour is critical for contemporary firms to fully derive benefits from investments in an enterprise system (ES). Based on the adaptive structuration theory (AST), it is proposed that employees’ exploration of an ES is mainly influenced by three fundamental components at different theoretical levels: task, technology, and the organisational environment. Accordingly, we develop a multilevel research model to interpret how task variety, system modularity, and local management commitment jointly affect employees’ system exploration. Our model is tested with a survey of ES users in 35 business units of six firms that have already implemented enterprise resource planning (ERP) systems, and several meaningful findings were discovered. At the individual level, both system modularity and task variety can directly affect employees’ system exploration, and the direct effect of system modularity is positively moderated by task variety. Further, unit-level local management commitment can either directly affect, or positively moderate, the relationship between task variety and system exploration. Limitations and implications for research and practice are discussed.

This study investigates the motivational factors affecting the quantity and quality of voluntary knowledge contribution in online Q&A communities. Although previous studies focus on knowledge contribution quantity, this study regards quantity and quality as two important, yet distinct, aspects of knowledge contribution. Drawing on self‐determination theory, this study proposes that five motivational factors, categorized along the extrinsic‐intrinsic spectrum of motivation, have differential effects on knowledge contribution quantity versus quality in the context of online Q&A communities. An online survey with 367 participants was conducted in a leading online Q&A community to test the research model. Results show that rewards in the reputation system, learning, knowledge self‐efficacy, and enjoy helping stand out as important motivations. Furthermore, rewards in the reputation system, as a manifestation of the external regulation, is more effective in facilitating the knowledge contribution quantity than quality. Knowledge self‐efficacy, as a manifestation of intrinsic motivation, is more strongly related to knowledge contribution quality, whereas the other intrinsic motivation, enjoy helping, is more strongly associated with knowledge contribution quantity. Both theoretical and practical implications are discussed.

With the recognition of the importance of organizational knowledge management (KM), researchers have paid increasing attention to knowledge management systems (KMS). However, since most prior studies were conducted in the context of Western societies, we know little about KMS diffusion in other regional contexts. Moreover, even with the increasing recognition of the influence of social factors in KM practices, there is a dearth of studies that examine how unique social cultural factors affect KMS diffusion in specific countries. To fill in this gap, this study develops an integrated framework, with special consideration on the influence of social cultures, to understand KMS diffusion in Chinese enterprises. In our framework, we examine how specific technological, organizational, and social cultural factors can influence the three-stage KMS diffusion process, that is, initiation, adoption, and routinization. This study provides a holistic view of the KMS diffusion in Chinese enterprises with practical guidance for successful KMS implementation.