Judging a Book by Its Cover: The Effect of Facial Perception on Centrality in Social Networks

Facial appearance matters in social networks. Individuals frequently make trait judgments from facial clues. Although these face-based impressions lack the evidence to determine validity, they are of vital importance, because they may relate to human network-based social behavior, such as seeking certain individuals for help, advice, dating, and cooperation, and thus they may relate to centrality in social networks. However, little to no work has investigated the apparent facial traits that influence network centrality, despite the large amount of research on attributions of the central position including personality and behavior. In this paper, we examine whether perceived traits based on facial appearance affect network centrality by exploring the initial stage of social network formation in a first-year college residential area. We took face photos of participants who are freshmen living in the same residential area, and we asked them to nominate community members linking to different networks. We then collected facial perception data by requiring other participants to rate facial images for three main attributions: dominance, trustworthiness, and attractiveness. Meanwhile, we proposed a framework to discover how facial appearance affects social networks. Our results revealed that perceived facial traits were correlated with the network centrality and that they were indicative to predict the centrality of people in different networks. Our findings provide psychological evidence regarding the interaction between faces and network centrality. Our findings also offer insights in to a combination of psychological and social network techniques, and they highlight the function of facial bias in cuing and signaling social traits. To the best of our knowledge, we are the first to explore the influence of facial perception on centrality in social networks.Comment: 11 pages, 8 figure

A self-starting bi-chromatic LiNbO_3 soliton microcomb

The wide range of functions that are possible with lithium niobate (LN) waveguide devices, including phase and intensity modulation, second-harmonic generation, and difference-frequency generation, makes it attractive as a potential microcomb material. LN microcombs would combine essential comb self-referencing and control functions with the pulse generation process in a single microresonator device. Here, we demonstrate a soliton microcomb in a monolithic high-Q LN resonator. Direct frequency doubling of the soliton spectrum is observed inside the same cavity. The LN soliton mode-locking process also self-starts and allows bi-directional switching of soliton states, effects that are shown to result from the LN photorefractive effect. The Kerr solitons exhibit a self-frequency shift resulting from the Raman effect of LN. This microcomb platform can dramatically simplify miniature time keeping, frequency synthesis/division, and spectroscopy systems. Moreover, direct generation of femtosecond timescale pulses within LN microresonators can benefit quantum photonics and signal processing systems

Rethinking Memory and Communication Cost for Efficient Large Language Model Training

Recently, various distributed strategies for large language model training have been proposed. However, these methods provided limited solutions for the trade-off between memory consumption and communication cost. In this paper, we rethink the impact of memory consumption and communication costs on the training speed of large language models, and propose a memory-communication balanced strategy set Partial Redundancy Optimizer (PaRO). PaRO provides comprehensive options which reduces the amount and frequency of inter-group communication with minor memory redundancy by fine-grained sharding strategy, thereby improving the training efficiency in various training scenarios. Additionally, we propose a Hierarchical Overlapping Ring (HO-Ring) communication topology to enhance communication efficiency between nodes or across switches in large language model training. Our experiments demonstrate that PaRO significantly improves training throughput by 1.19x-2.50x compared to the SOTA method and achieves a near-linear scalability. The HO-Ring algorithm improves communication efficiency by 36.5% compared to the traditional Ring algorithm