50 research outputs found
Improve Transformer Pre-Training with Decoupled Directional Relative Position Encoding and Representation Differentiations
In this work, we revisit the Transformer-based pre-trained language models
and identify two problems that may limit the expressiveness of the model.
Firstly, existing relative position encoding models (e.g., T5 and DEBERTA)
confuse two heterogeneous information: relative distance and direction. It may
make the model unable to capture the associative semantics of the same
direction or the same distance, which in turn affects the performance of
downstream tasks. Secondly, we notice the pre-trained BERT with Mask Language
Modeling (MLM) pre-training objective outputs similar token representations and
attention weights of different heads, which may impose difficulties in
capturing discriminative semantic representations. Motivated by the above
investigation, we propose two novel techniques to improve pre-trained language
models: Decoupled Directional Relative Position (DDRP) encoding and MTH
pre-training objective. DDRP decouples the relative distance features and the
directional features in classical relative position encoding for better
position information understanding. MTH designs two novel auxiliary losses
besides MLM to enlarge the dissimilarities between (a) last hidden states of
different tokens, and (b) attention weights of different heads, alleviating
homogenization and anisotropic problem in representation learning for better
optimization. Extensive experiments and ablation studies on GLUE benchmark
demonstrate the effectiveness of our proposed methods
Mastering Complex Control in MOBA Games with Deep Reinforcement Learning
We study the reinforcement learning problem of complex action control in the
Multi-player Online Battle Arena (MOBA) 1v1 games. This problem involves far
more complicated state and action spaces than those of traditional 1v1 games,
such as Go and Atari series, which makes it very difficult to search any
policies with human-level performance. In this paper, we present a deep
reinforcement learning framework to tackle this problem from the perspectives
of both system and algorithm. Our system is of low coupling and high
scalability, which enables efficient explorations at large scale. Our algorithm
includes several novel strategies, including control dependency decoupling,
action mask, target attention, and dual-clip PPO, with which our proposed
actor-critic network can be effectively trained in our system. Tested on the
MOBA game Honor of Kings, our AI agent, called Tencent Solo, can defeat top
professional human players in full 1v1 games.Comment: AAAI 202
Optimization of a static headspace GC-MS method and its application in metabolic fingerprinting of the leaf volatiles of 42 citrus cultivars
Citrus leaves, which are a rich source of plant volatiles, have the beneficial attributes of rapid growth, large biomass, and availability throughout the year. Establishing the leaf volatile profiles of different citrus genotypes would make a valuable contribution to citrus species identification and chemotaxonomic studies. In this study, we developed an efficient and convenient static headspace (HS) sampling technique combined with gas chromatography-mass spectrometry (GC-MS) analysis and optimized the extraction conditions (a 15-min incubation at 100 ˚C without the addition of salt). Using a large set of 42 citrus cultivars, we validated the applicability of the optimized HS-GC-MS system in determining leaf volatile profiles. A total of 83 volatile metabolites, including monoterpene hydrocarbons, alcohols, sesquiterpene hydrocarbons, aldehydes, monoterpenoids, esters, and ketones were identified and quantified. Multivariate statistical analysis and hierarchical clustering revealed that mandarin (Citrus reticulata Blanco) and orange (Citrus sinensis L. Osbeck) groups exhibited notably differential volatile profiles, and that the mandarin group cultivars were characterized by the complex volatile profiles, thereby indicating the complex nature and diversity of these mandarin cultivars. We also identified those volatile compounds deemed to be the most useful in discriminating amongst citrus cultivars. This method developed in this study provides a rapid, simple, and reliable approach for the extraction and identification of citrus leaf volatile organic compound, and based on this methodology, we propose a leaf volatile profile-based classification model for citrus
Expert consensus on spontaneous ventilation video-assisted thoracoscopic surgery in primary spontaneous pneumothorax (Guangzhou)
Tubeless video-assisted thoracic surgery for pulmonary ground-glass nodules: expert consensus and protocol (Guangzhou)
Aridity-driven shift in biodiversity–soil multifunctionality relationships
From Springer Nature via Jisc Publications RouterHistory: received 2021-01-07, accepted 2021-08-12, registration 2021-08-25, pub-electronic 2021-09-09, online 2021-09-09, collection 2021-12Publication status: PublishedFunder: National Natural Science Foundation of China (National Science Foundation of China); doi: https://doi.org/10.13039/501100001809; Grant(s): 31770430Abstract: Relationships between biodiversity and multiple ecosystem functions (that is, ecosystem multifunctionality) are context-dependent. Both plant and soil microbial diversity have been reported to regulate ecosystem multifunctionality, but how their relative importance varies along environmental gradients remains poorly understood. Here, we relate plant and microbial diversity to soil multifunctionality across 130 dryland sites along a 4,000 km aridity gradient in northern China. Our results show a strong positive association between plant species richness and soil multifunctionality in less arid regions, whereas microbial diversity, in particular of fungi, is positively associated with multifunctionality in more arid regions. This shift in the relationships between plant or microbial diversity and soil multifunctionality occur at an aridity level of ∼0.8, the boundary between semiarid and arid climates, which is predicted to advance geographically ∼28% by the end of the current century. Our study highlights that biodiversity loss of plants and soil microorganisms may have especially strong consequences under low and high aridity conditions, respectively, which calls for climate-specific biodiversity conservation strategies to mitigate the effects of aridification