Genetic Characteristics of Three Unique Recombinant Forms of HIV-1 in Yunnan, China

Abstract

Learning from human feedback has shown success in aligning large, pretrained models with human values. Prior works have mostly focused on learning from high-level labels, such as preferences between pairs of model outputs. On the other hand, many domains could benefit from more involved, detailed feedback, such as revisions, explanations, and reasoning of human users. Our work proposes using nuanced feedback through the form of human revisions for stronger alignment. In this paper, we ask expert designers to fix layouts generated from a generative layout model that is pretrained on a large-scale dataset of mobile screens. Then, we train a reward model based on how human designers revise these generated layouts. With the learned reward model, we optimize our model with reinforcement learning from human feedback (RLHF). Our method, Revision-Aware Reward Models (RARE), allows a generative text-to-layout model to produce more modern, designer-aligned layouts, showing the potential for utilizing human revisions and stronger forms of feedback in improving generative models.

Second-Generation SARS-CoV-2 Recombinants: Lessons from Other Viruses

“…x subtype B [26], CRF07_BC x CRF83_cpx [27], and CRF86_BC x a URF [28] also from in China. HTLV [29].…”

“…The double infection between CRFs established more optimal conditions for the second-generation recombination, and many second-generation recombinants were represented by the recombination of CRF01_AE and CRF07_BC fragments, such as CRF79_0107 [ 11 ], CRF80_0107 [ 12 ], CRF102_01075 [ 13 ], CRF109_0107 [ 14 ], CR125_0107 [ 15 ], and others [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. Other instances include CRF01_AE x CRF08_BC [ 25 ], CRF01_AE x subtype B [ 26 ], CRF07_BC x CRF83_cpx [ 27 ], and CRF86_BC x a URF [ 28 ] also from in China. HTLV [ 29 ].…”