Large language models have been used to simulate human society using multi-agent systems. Most current social simulation research emphasizes interactive behaviors in fixed environments, ignoring information opacity, relationship variability and diffusion diversity. In this paper, we study the dynamics of information diffusion in 12 asymmetric open environments defined by information content and distribution mechanisms. We first present a general framework to capture the features of information diffusion. Then, we designed a dynamic attention mechanism to help agents allocate attention to different information, addressing the limitations of LLM-based attention. Agents start by responding to external information stimuli within a five-agent group, increasing group size and forming information circles while developing relationships and sharing information. Additionally, we observe the emergence of information cocoons, the evolution of information gaps, and the accumulation of social capital, which are closely linked to psychological, sociological, and communication theories.

Recent advancements in large language models (LLMs) have shown potential for transforming data processing in healthcare, particularly in understanding complex clinical narratives. This study evaluates the efficacy of zero-shot LLMs in summarizing long clinical texts that require temporal reasoning, a critical aspect for comprehensively capturing patient histories and treatment trajectories. We applied a series of advanced zero-shot LLMs to extensive clinical documents, assessing their ability to integrate and accurately reflect temporal dynamics without prior task-specific training. While the models efficiently identified key temporal events, they struggled with chronological coherence over prolonged narratives. The evaluation, combining quantitative and qualitative methods, highlights the strengths and limitations of zero-shot LLMs in clinical text summarization. The results suggest that while promising, zero-shot LLMs require further refinement to effectively support clinical decision-making processes, underscoring the need for enhanced model training approaches that better capture the nuances of temporal information in long context medical documents.

Weight pruning is a powerful technique to realize model compression. We propose PCNN, a fine-grained regular 1D pruning method. A novel index format called Sparsity Pattern Mask (SPM) is presented to encode the sparsity in PCNN. Leveraging SPM with limited pruning patterns and non-zero sequences with equal length, PCNN can be efficiently employed in hardware. Evaluated on VGG-16 and ResNet-18, our PCNN achieves the compression rate up to 8.4X with only 0.2% accuracy loss. We also implement a pattern-aware architecture in 55nm process, achieving up to 9.0X speedup and 28.39 TOPS/W efficiency with only 3.1% on-chip memory overhead of indices.

Deep learning for supervised learning has achieved astonishing performance in various machine learning applications. However, annotated data is expensive and rare. In practice, only a small portion of data samples are annotated. Pseudo-ensembling-based approaches have achieved state-of-the-art results in computer vision related tasks. However, it still relies on the quality of an initial model built by labeled data. Less labeled data may degrade model performance a lot. Domain constraint is another way regularize the posterior but has some limitation. In this paper, we proposed a fuzzy domain-constraint-based framework which loses the requirement of traditional constraint learning and enhances the model quality for semi supervision. Simulations results show the effectiveness of our design.