CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning

Offline reinforcement learning (RL) aims to learn an optimal policy from pre-collected and labeled datasets, which eliminates the time-consuming data collection in online RL. However, offline RL still bears a large burden of specifying/handcrafting extrinsic rewards for each transition in the offline data. As a remedy for the labor-intensive labeling, we propose to endow offline RL tasks with a few expert data and utilize the limited expert data to drive intrinsic rewards, thus eliminating the need for extrinsic rewards. To achieve that, we introduce \textbf{C}alibrated \textbf{L}atent g\textbf{U}idanc\textbf{E} (CLUE), which utilizes a conditional variational auto-encoder to learn a latent space such that intrinsic rewards can be directly qualified over the latent space. CLUE's key idea is to align the intrinsic rewards consistent with the expert intention via enforcing the embeddings of expert data to a calibrated contextual representation. We instantiate the expert-driven intrinsic rewards in sparse-reward offline RL tasks, offline imitation learning (IL) tasks, and unsupervised offline RL tasks. Empirically, we find that CLUE can effectively improve the sparse-reward offline RL performance, outperform the state-of-the-art offline IL baselines, and discover diverse skills from static reward-free offline data

Structure-aware deep model for MHC-II peptide binding affinity prediction

Abstract The prediction of major histocompatibility complex (MHC)-peptide binding affinity is an important branch in immune bioinformatics, especially helpful in accelerating the design of disease vaccines and immunity therapy. Although deep learning-based solutions have yielded promising results on MHC-II molecules in recent years, these methods ignored structure knowledge from each peptide when employing the deep neural network models. Each peptide sequence has its specific combination order, so it is worth considering adding the structural information of the peptide sequence to the deep model training. In this work, we use positional encoding to represent the structural information of peptide sequences and validly combine the positional encoding with existing models by different strategies. Experiments on three datasets show that the introduction of position-coding information can further improve the performance built upon the existing model. The idea of introducing positional encoding to this field can provide important reference significance for the optimization of the deep network structure in the future

Promotion of Soil Microbial Community Restoration in the Mu Us Desert (China) by Aerial Seeding

Soil microbial communities link soil and plants and play a key role in connecting above-ground and below-ground communities in terrestrial ecosystems. Currently, how artificial revegetation promotes the restoration of soil microbial community diversity in degraded ecosystems attracts extensive attention. In this study, soil samples were collected from long-term artificially restored mobile sandy lands (aerial seeding sample plots) from 1983 to 2015 in the Mu Us Desert. The second-generation high-throughput sequencing technology was adopted to identify soil microorganisms and analyze the changes in their community structure and diversity. The relationships between changes in microbial diversity and soil nutrients were explored by Pearson correlation analysis and canonical correspondence analysis. In addition, the restoration of subsurface soil microbial communities in this area was evaluated. The results are as follows: (1) The alpha diversity of the soil microorganisms increased significantly with the restoration period, and the composition and diversity of the soil microbial communities in the sample plots in different restoration years varied significantly. (2) Soil nutrient indexes, such as total carbon, total nitrogen and nitrate nitrogen, significantly increased with the restoration period and were significantly positively correlated with soil fungal and bacterial diversity. (3) Key soil fungal and bacterial phyla contributed to nutrient cycling in degraded ecosystems. It can be concluded that afforestation by aerial seeding facilitates the change in community structure and increases the diversity of soil microorganisms in the Mu Us Desert. This paper provides a basis for future measures and policies for restoring degraded lands and ecosystems

Promotion of Soil Microbial Community Restoration in the Mu Us Desert (China) by Aerial Seeding

Soil microbial communities link soil and plants and play a key role in connecting above-ground and below-ground communities in terrestrial ecosystems. Currently, how artificial revegetation promotes the restoration of soil microbial community diversity in degraded ecosystems attracts extensive attention. In this study, soil samples were collected from long-term artificially restored mobile sandy lands (aerial seeding sample plots) from 1983 to 2015 in the Mu Us Desert. The second-generation high-throughput sequencing technology was adopted to identify soil microorganisms and analyze the changes in their community structure and diversity. The relationships between changes in microbial diversity and soil nutrients were explored by Pearson correlation analysis and canonical correspondence analysis. In addition, the restoration of subsurface soil microbial communities in this area was evaluated. The results are as follows: (1) The alpha diversity of the soil microorganisms increased significantly with the restoration period, and the composition and diversity of the soil microbial communities in the sample plots in different restoration years varied significantly. (2) Soil nutrient indexes, such as total carbon, total nitrogen and nitrate nitrogen, significantly increased with the restoration period and were significantly positively correlated with soil fungal and bacterial diversity. (3) Key soil fungal and bacterial phyla contributed to nutrient cycling in degraded ecosystems. It can be concluded that afforestation by aerial seeding facilitates the change in community structure and increases the diversity of soil microorganisms in the Mu Us Desert. This paper provides a basis for future measures and policies for restoring degraded lands and ecosystems