Exact Single-Source SimRank Computation on Large Graphs

SimRank is a popular measurement for evaluating the node-to-node similarities based on the graph topology. In recent years, single-source and top-$k$ SimRank queries have received increasing attention due to their applications in web mining, social network analysis, and spam detection. However, a fundamental obstacle in studying SimRank has been the lack of ground truths. The only exact algorithm, Power Method, is computationally infeasible on graphs with more than $10^6$ nodes. Consequently, no existing work has evaluated the actual trade-offs between query time and accuracy on large real-world graphs. In this paper, we present ExactSim, the first algorithm that computes the exact single-source and top-$k$ SimRank results on large graphs. With high probability, this algorithm produces ground truths with a rigorous theoretical guarantee. We conduct extensive experiments on real-world datasets to demonstrate the efficiency of ExactSim. The results show that ExactSim provides the ground truth for any single-source SimRank query with a precision up to 7 decimal places within a reasonable query time.Comment: ACM SIGMOD 202

A85: A Study on the Influence of Different Media Choices on People\u27s Exercise Behavior Post COVID-19

Purpose: As modern technology advances swiftly, the significance of mobile media in people\u27s lives is increasingly pronounced. Fitness software, rooted in mobile media, has permeated every facet of daily life, reshaping not only the traditional modes of exercise but also giving rise to a distinctive exercise culture. In the context of epidemic prevention and control, this study examined the impact of various media on individuals\u27 exercise behavior, exploring how people\u27s choice of media influences their post-epidemic exercise possibilities and self-efficacy. Methods: This study extensively reviewed literature to identify the questionnaire\u27s origin and employs exercise behavior theories to construct a conceptual model illustrating its impact on individuals\u27 exercise behavior. Data collection utilized the questionnaire method, and the gathered information was subsequently analyzed through mathematical and statistical software, including Excel and SPSS. Results: People were no longer satisfied with a single new media when choosing media, but more often chose a combination of media with group participation media represented by WeChat and Weibo and interactive operation media represented by VR and computer games. The factors that influence people\u27s exercise behavior were: gender, age, time spent in contact with the media, and exercise habits. The correlation, regression analysis and structural equation modeling showed that there was a significant positive relationship between the likelihood of exercise and exercise behavior (β=1.232, p \u3c 0.001). The correlation of exercise and self-efficacy on exercise behavior was negative and significant (β=-0.059, p \u3c 0.01). Conclusions: The outbreak has increased people\u27s exposure to the media and has also led to a greater interest in the exercise field through a diverse range of media. The choice of different media can promote people\u27s exercise behavior. The role of the media in promoting people\u27s exercise behavior still needs to be improved

WACO: Word-Aligned Contrastive Learning for Speech Translation

End-to-end Speech Translation (E2E ST) aims to directly translate source speech into target text. Existing ST methods perform poorly when only extremely small speech-text data are available for training. We observe that an ST model's performance closely correlates with its embedding similarity between speech and source transcript. In this paper, we propose Word-Aligned COntrastive learning (WACO), a simple and effective method for extremely low-resource speech-to-text translation. Our key idea is bridging word-level representations for both speech and text modalities via contrastive learning. We evaluate WACO and other methods on the MuST-C dataset, a widely used ST benchmark, and on a low-resource direction Maltese-English from IWSLT 2023. Our experiments demonstrate that WACO outperforms the best baseline by 9+ BLEU points with only 1-hour parallel ST data. Code is available at https://github.com/owaski/WACO.Comment: ACL 2023 Poste

Molecular Lines of 13 Galactic Infrared Bubble Regions

We investigated the physical properties of molecular clouds and star formation processes around infrared bubbles which are essentially expanding HII regions. We performed observations of 13 galactic infrared bubble fields containing 18 bubbles. Five molecular lines, 12CO (J=1-0), 13CO (J=1-0), C18O(J=1-0), HCN (J=1-0), and HCO+ (J=1-0), were observed, and several publicly available surveys, GLIMPSE, MIPSGAL, ATLASGAL, BGPS, VGPS, MAGPIS, and NVSS, were used for comparison. We find that these bubbles are generally connected with molecular clouds, most of which are giant. Several bubble regions display velocity gradients and broad shifted profiles, which could be due to the expansion of bubbles. The masses of molecular clouds within bubbles range from 100 to 19,000 solar mass, and their dynamic ages are about 0.3-3.7 Myr, which takes into account the internal turbulence pressure of surrounding molecular clouds. Clumps are found in the vicinity of all 18 bubbles, and molecular clouds near four of these bubbles with larger angular sizes show shell-like morphologies, indicating that either collect-and-collapse or radiation-driven implosion processes may have occurred. Due to the contamination of adjacent molecular clouds, only six bubble regions are appropriate to search for outflows, and we find that four of them have outflow activities. Three bubbles display ultra-compact HII regions at their borders, and one of them is probably responsible for its outflow. In total, only six bubbles show star formation activities in the vicinity, and we suggest that star formation processes might have been triggered.Comment: 55 Pages, 32 figures. Accepted for publication in A