Traditional force-based graph layout models are rooted in virtual physics, while criteria-driven techniques position nodes by directly optimizing graph readability criteria. In this article, we systematically explore the integration of these two approaches, introducing criteria-driven force-based graph layout techniques. We propose a general framework that, based on user-specified readability criteria, such as minimizing edge crossings, automatically constructs a force-based model tailored to generate layouts for a given graph. Models derived from highly similar graphs can be reused to create initial layouts, users can further refine layouts by imposing different criteria on subgraphs. We perform quantitative comparisons between our layout methods and existing techniques across various graphs and present a case study on graph exploration. Our results indicate that our framework generates superior layouts compared to existing techniques and exhibits better generalization capabilities than deep learning-based methods.

This paper addresses the challenge of insufficient sampling diversity in large language models (LLMs) under the conventional autoregressive decoding framework. Our research reveals that, in some tasks where LLMs underperform, they actually possess the capability to provide correct answers. However, this potential is limited by inadequate sampling diversity in the decoding process, which prevents the models from effectively and fully leveraging their reasoning capabilities. To address this issue, we propose the Adaptive Trigram Model-Assisted Decoding Strategy (ATM-ADS), a method designed to enhance the reasoning abilities of large models without requiring fine-tuning. By dynamically providing diverse candidate vocabularies, combined with a decoding dynamic decision-making mechanism and an adaptive smoothing strategy, our approach substantially enhances sampling diversity during the decoding process. Experimental results demonstrate that our ATM-ADS significantly improves model performance across multiple task scenarios, highlighting its broad potential for cross-task and cross-linguistic applications.

With the rapid development of sensing technologies, massive spatiotemporal data have been acquired from the urban space with respect to different domains, such as transportation and environment. Numerous co-occurrence patterns (e.g., traffic speed <; 10km/h, weather = foggy, and air quality = unhealthy) between the transportation data and other types of data can be obtained with given spatiotemporal constraints (e.g., within 3 kilometers and lasting for 2 hours) from these heterogeneous data sources. Such patterns present valuable implications for many urban applications, such as traffic management, pollution diagnosis, and transportation planning. However, extracting and understanding these patterns is beyond manual capability because of the scale, diversity, and heterogeneity of the data. To address this issue, a novel visual analytics system called CorVizor is proposed to identify and interpret these co-occurrence patterns. CorVizor comprises two major components. The first component is a co-occurrence mining framework involving three steps, namely, spatiotemporal indexing, co-occurring instance generation, and pattern mining. The second component is a visualization technique called CorView that implements a level-of-detail mechanism by integrating tailored visualizations to depict the extracted spatiotemporal co-occurrence patterns. The case studies and expert interviews are conducted to demonstrate the effectiveness of CorVizor.