Automated LEGO® design is challenging due to the extensive variety of LEGO® brick types and the necessity of constructing semantically meaningful models from individually meaningless components. Current automatic LEGO® generation methods face two key challenges: i) They typically rely on explicit modeling of brick connectivity to ensure structural validity. However, this requires extensive manual annotation, which is labor-intensive as the variety of LEGO® primitives increases. This limits training data diversity, restricting the variety of LEGO® bricks that can be effectively utilized. ii) To facilitate learning within neural networks, current methods often employ either volume or text-based descriptions to represent LEGO® models. However, volumetric representations are computationally expensive and hamper large-scale generative training, while text-based approaches rely on large language models and dedicated text-to-brick mapping rules, introducing a semantic gap between language tokens and 3D brick structures.

Recently, significant progress has been made in condition-guided human motion generation. However, due to the inherent abstraction of conditional semantics like text, music and trajectory, these methods often fall short of generating precise motions that align with human intent. In contrast, free-hand sketches inherently and accurately depict human perspective intent, finding extensive applications across multiple domains. In this article, we introduce Sketch-guided human Motion Diffusion (SMD), to address a novel scenario: sketch-to-motion, aiming to generate plausible and natural human motions based on human motion sketches. Specifically, Our proposed SMD employs a Dual-branch Time-aware Transformer that utilizes both global semantic and local perspective level attention to condition 2D sketch information for 3D motion generation. At the global semantic level, we establish associations between the representation of the entire sketch and the sequential motion to ensure the generated motion aligns with the semantic content of the sketch. Meanwhile, at the local perspective level, a sketch-aware local attention is devised to correlate the sketch patches with the motion keyframes, aiming to precisely align the keyframes with the given sketches. Rooted in Diffusion model and Dual-branch Time-aware Transformer, our approach demonstrates proficiency in motion in-betweening and body part editing tasks, seamlessly generating natural motion sequences that harmonize with the provided context. Multiple experiments conducted on the curated sketch-to-motion datasets validate the efficacy of SMD, showcasing the state-of-the-art generation performances.

To satisfy prohibitively massive computational requirements of current deep Convolutional Neural Networks (CNNs), CNN-specific accelerators are widely deployed in large-scale systems. Caused by high-energy neutrons and α-particle strikes, soft error may lead to catastrophic failures when CNN is deployed on high integration density accelerators. As CNNs become ubiquitous in mission-critical domains, ensuring the reliable execution of CNN accelerators in the presence of soft errors is increasingly essential. In this article, we propose to Re cycle I dle P rocessing E lements (PEs) in the CNN accelerator for vulnerable filters soft error detection (ReIPE). Considering the error-sensitivity of filters, ReIPE first carries out a filter-level gradient analysis process to replace fault injection for fast filter-wise error resilience estimation. Then, to achieve maximal reliability benefits, combining the hardware-level systolic array idleness and software-level CNN filter-wise error resilience profile, ReIPE preferentially duplicated loads the most vulnerable filters onto systolic array to recycle idle-column PEs for opportunistically redundant execution (error detection). Exploiting the data reuse properties of accelerators, ReIPE incorporates the error detection process into the original computation flow of accelerators to perform real-time error detection. Once the error is detected, ReIPE will trigger a correction round to rectify the erroneous output. Experimental results performed on LeNet-5, Cifar-10-CNN, AlexNet, ResNet-20, VGG-16, and ResNet-50 exhibit that ReIPE can cover 96.40% of errors while reducing 75.06% performance degradation and 67.79% energy consumption of baseline dual modular redundancy on average. Moreover, to satisfy the reliability requirements of various application scenarios, ReIPE is also applicable for pruned, quantized, and Transformer-based models, as well as portable to other accelerator architectures.

Rumors mislead judgments of people, affect economic development, and the stability of social order. The research on the rule of spreading rumors is significant and meaningful. This paper improves the traditional Barabási–Albert scale-free network and proposes a network topology model that conforms to the characteristics of sharing social networks based on the complex network theory and the actual characteristics of sharing social networks. In addition, the credulous spider rational taciturn rumor propagation model is proposed by improving the credulous spider rational model, which solves the overspread problem of the traditional rumor propagation model. This paper further studies the influence of anxiety on the spread of rumors, and finds that the anxiety of audience is increasing with the spread degree of rumors.