Mengdi Liu

Mengdi Liu, Zhangyang Gao, Hong Chang et al.

Understanding how genes influence phenotype across species is a fundamental challenge in genetic engineering, which will facilitate advances in various fields such as crop breeding, conservation biology, and personalized medicine. However, current phenotype prediction models are limited to individual species and expensive phenotype labeling process, making the genotype-to-phenotype prediction a highly domain-dependent and data-scarce problem. To this end, we suggest taking images as morphological proxies, facilitating cross-species generalization through large-scale multimodal pretraining. We propose the first genotype-to-phenotype diffusion model (G2PDiffusion) that generates morphological images from DNA considering two critical evolutionary signals, i.e., multiple sequence alignments (MSA) and environmental contexts. The model contains three novel components: 1) a MSA retrieval engine that identifies conserved and co-evolutionary patterns; 2) an environment-aware MSA conditional encoder that effectively models complex genotype-environment interactions; and 3) an adaptive phenomic alignment module to improve genotype-phenotype consistency. Extensive experiments show that integrating evolutionary signals with environmental context enriches the model's understanding of phenotype variability across species, thereby offering a valuable and promising exploration into advanced AI-assisted genomic analysis.

Hongjia Wu, Mengdi Liu

Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in personal mobile devices, from smartphones towards wearable devices. Wearable devices come in many different forms targeting different application scenarios. Among these, the fitness wearable devices (FWDs) are proven to be one of the forms that intrigue the market and occupy an increasing trend in terms of the market share. Nevertheless, although the fitness wearable devices nowadays are functionally self-contained based on the advanced sensor, computation, and communicative technologies, there is still a large gap to truly satisfy the target customer group, i.e., accessible to and usable by a larger quantity of users. This fuels the research area on applying the universal design principles to fitness wearable devices. In this survey, we first present the background of FWDs and show the acceptance and adaption challenges of the corresponding user groups. We then review the universal design principle and how it and its relative approaches could be used in FWDs. Further, we collect the available FWDs that bear the universal design principles in their development circles. Last, we open up the discussion based on the surveyed literature and provide the insight of potential future work.

Mengdi Liu, Guangquan Xu

Cold Start (CS) and sparse evaluation problems dramatically degrade recommendation performance in large-scale recommendation systems such as Taobao and eBay. We name this degradation as the sparse trust problem, which will cause the decrease of the recommendation accuracy rate. To address this problem we propose a novel sparse trust mining method, which is based on the Roundtable Gossip Algorithm (RGA). First, we define the relevant representation of sparse trust, which provides a research idea to solve the problem of sparse evidence in the large-scale recommendation system. Based on which the RGA is proposed for mining latent sparse trust relationships between entities in large-scale recommendation systems. Second, we propose an efficient and simple anti-sparsification method, which overcomes the disadvantages of random trust relationship propagation and Grade Inflation caused by different users have different standard for item rating. Finally, the experimental results show that our method can effectively mine new trust relationships and mitigate the sparse trust problem.