Pop Quiz! Do Pre-trained Code Models Possess Knowledge of Correct API Names?

Recent breakthroughs in pre-trained code models, such as CodeBERT and Codex, have shown their superior performance in various downstream tasks. The correctness and unambiguity of API usage among these code models are crucial for achieving desirable program functionalities, requiring them to learn various API fully qualified names structurally and semantically. Recent studies reveal that even state-of-the-art pre-trained code models struggle with suggesting the correct APIs during code generation. However, the reasons for such poor API usage performance are barely investigated. To address this challenge, we propose using knowledge probing as a means of interpreting code models, which uses cloze-style tests to measure the knowledge stored in models. Our comprehensive study examines a code model's capability of understanding API fully qualified names from two different perspectives: API call and API import. Specifically, we reveal that current code models struggle with understanding API names, with pre-training strategies significantly affecting the quality of API name learning. We demonstrate that natural language context can assist code models in locating Python API names and generalize Python API name knowledge to unseen data. Our findings provide insights into the limitations and capabilities of current pre-trained code models, and suggest that incorporating API structure into the pre-training process can improve automated API usage and code representations. This work provides significance for advancing code intelligence practices and direction for future studies. All experiment results, data and source code used in this work are available at \url{https://doi.org/10.5281/zenodo.7902072}

Microbial responses to inorganic nutrient amendment overridden by warming: Consequences on soil carbon stability.

Eutrophication and climate warming, induced by anthropogenic activities, are simultaneously occurring worldwide and jointly affecting soil carbon stability. Therefore, it is of great interest to examine whether and how they interactively affect soil microbial community, a major soil carbon driver. Here, we showed that climate warming, simulated by southward transferring Mollisol soil in agricultural ecosystems from the cold temperate climate zone (N) to warm temperate climate (C) and subtropical climate zone (S), decreased soil organic matter (SOM) by 6%-12%. In contrast, amendment with nitrogen, phosphorus and potassium enhanced plant biomass by 97% and SOM by 6% at the N site, thus stimulating copiotrophic taxa but reducing oligotrophic taxa in relative abundance. However, microbial responses to nutrient amendment were overridden by soil transfer in that nutrient amendment had little effect at the C site but increased recalcitrant carbon-degrading fungal Agaricomycetes and Microbotryomycetes taxa derived from Basidiomycota by 4-17 folds and recalcitrant carbon-degrading genes by 23%-40% at the S site, implying a possible priming effect. Consequently, SOM at the S site was not increased by nutrient amendment despite increased plant biomass by 108%. Collectively, we demonstrate that soil transfer to warmer regions overrides microbial responses to nutrient amendment and weakens soil carbon sequestration

Real-Valued Direct Position Determination of Quasi-Stationary Signals for Nested Arrays: Khatri–Rao Subspace and Unitary Transformation

The features of quasi-stationary signals (QSS) are considered to be in a direct position determination (DPD) framework, and a real-valued DPD algorithm of QSS for nested arrays is proposed. By stacking the vectorization form of the signal’s covariance for different frames and further eliminating noise, a new noise-eliminated received signal matrix is obtained first. Then, the combination of the Khatri–Rao subspace method and subspace data fusion method was performed to form the cost function. High complexity can be reduced by matrix reconstruction, including the modification of the dimension-reduced matrix and unitary transformation. Ultimately, the advantage of lower complexity, compared with the previous algorithm, is verified by complexity analysis, and the superiority over the existing algorithms, in terms of the maximum number of identifiable sources, estimation accuracy, and resolution, are corroborated by some simulation results

Bacteriophage–prokaryote dynamics and interaction within anaerobic digestion processes across time and space

Abstract Background Bacteriophage–prokaryote dynamics and interaction are believed to be important in governing microbiome composition and ecosystem functions, yet our limited knowledge of the spatial and temporal variation in phage and prokaryotic community compositions precludes accurate assessment of their roles and impacts. Anaerobic digesters are ideal model systems to examine phage–host interaction, owing to easy access, stable operation, nutrient-rich environment, and consequently enormous numbers of phages and prokaryotic cells. Results Equipped with high-throughput, cutting-edge environmental genomics techniques, we examined phage and prokaryotic community composition of four anaerobic digesters in full-scale wastewater treatment plants across China. Despite the relatively stable process performance in biogas production, phage and prokaryotic groups fluctuated monthly over a year of study period, showing significant correlations between those two groups at the α- and β-diversity levels. Strikingly, phages explained 40.6% of total variations of the prokaryotic community composition, much higher than the explanatory power by abiotic factors (14.5%). Consequently, phages were significantly (P < 0.010) linked to parameters related to process performance including biogas production and volatile solid concentrations. Association network analyses showed phage–prokaryote pairs were shallowly conserved since they were detected only within small viral clades. Conclusions Those results collectively demonstrate phages as a major biotic factor in controlling prokaryotic composition and process performance. Therefore, phages may play a larger role in shaping prokaryotic community dynamics and process performance of anaerobic digesters than currently appreciated

Molecular mechanisms of water table lowering and nitrogen deposition in affecting greenhouse gas emissions from a Tibetan alpine wetland.

Rapid climate change and intensified human activities have resulted in water table lowering (WTL) and enhanced nitrogen (N) deposition in Tibetan alpine wetlands. These changes may alter the magnitude and direction of greenhouse gas (GHG) emissions, affecting the climate impact of these fragile ecosystems. We conducted a mesocosm experiment combined with a metagenomics approach (GeoChip 5.0) to elucidate the effects of WTL (-20&nbsp;cm relative to control) and N deposition (30&nbsp;kg N ha-1 &nbsp;yr-1 ) on carbon dioxide (CO2 ), methane (CH4 ) and nitrous oxide (N2 O) fluxes as well as the underlying mechanisms. Our results showed that WTL reduced CH4 emissions by 57.4% averaged over three growing seasons compared with no-WTL plots, but had no significant effect on net CO2 uptake or N2 O flux. N deposition increased net CO2 uptake by 25.2% in comparison with no-N deposition plots and turned the mesocosms from N2 O sinks to N2 O sources, but had little influence on CH4 emissions. The interactions between WTL and N deposition were not detected in all GHG emissions. As a result, WTL and N deposition both reduced the global warming potential (GWP) of growing season GHG budgets on a 100-year time horizon, but via different mechanisms. WTL reduced GWP from 337.3 to -480.1&nbsp;g CO2 -eq m-2 mostly because of decreased CH4 emissions, while N deposition reduced GWP from 21.0 to -163.8&nbsp;g CO2 -eq m-2 , mainly owing to increased net CO2 uptake. GeoChip analysis revealed that decreased CH4 production potential, rather than increased CH4 oxidation potential, may lead to the reduction in net CH4 emissions, and decreased nitrification potential and increased denitrification potential affected N2 O fluxes under WTL conditions. Our study highlights the importance of microbial mechanisms in regulating ecosystem-scale GHG responses to environmental changes