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Abstract Multispecies mutualistic interactions are ubiquitous and essential in nature, yet they face several threats, many of which have been exacerbated in the Anthropocene era. Understanding the factors that drive the stability and persistence of mutualism has become increasingly important in light of global change. Although dispersal is widely recognized as a crucial spatially explicit process in maintaining biodiversity and community structure, knowledge about how the dispersal of mutualists contributes to the persistence of mutualistic systems remains limited. In this study, we used a synthetic mutualism formed by genetically modified budding yeast to investigate the effect of dispersal on the persistence and stability of mutualisms under exploitation. We found that dispersal increased the persistence of exploited mutualisms by 80% compared to the isolated systems. Furthermore, our results showed that dispersal increased local diversity, decreased beta diversity among local communities, and stabilized community structure at the regional scale. Our results indicate that dispersal can allow mutualisms to persist in meta-communities by reintroducing species that are locally competitively excluded by exploiters. With limited dispersal, e.g. due to increased fragmentation of meta-communities, mutualisms might be more prone to breakdown. Taken together, our results highlight the critical role of dispersal in facilitating the persistence of mutualism. 

The concurrent execution of deep neural networks (DNN) inference tasks on intermittently-powered batteryless devices (IPDs) has recently garnered much attention due to its potential in a broad range of smart sensing applications. While the checkpointing mechanisms (CMs) provided by the state-of-the-art make this possible, scheduling inference tasks on IPDs is still a complex problem due to significant performance variations across DNN layers and CM choices. This complexity is further accentuated by dynamic environmental conditions and inherent resource constraints of IPDs. To tackle these challenges, we present MII, a framework designed for intermittence-aware inference and scheduling on IPDs. MII formulates the shutdown and live time functions of an IPD from profiling data, which our offline intermittence-aware search scheme uses to find optimal layer-wise CMs for each task. At runtime, MII enhances job success rates by dynamically making scheduling decisions to mitigate workload losses from power interruptions and adjusting these CMs in response to actual energy patterns. Our evaluation demonstrates the superiority of MII over the state-of-the-art. In controlled environments, MII achieves an average increase of 21% and 39% in successful jobs under stable and dynamic energy patterns. In real-world settings, MII achieves 33% and 24% more successful jobs indoors and outdoors. 

Two-dimensional (2D) transition metal carbides, nitrides and carbonitrides, known as MXenes, are of interest as electrocatalysts. Tungsten-based MXenes are predicted to have low overpotentials in the hydrogen evolution reaction but their synthesis has proven difficult due to the calculated instability of their hypothetical MAX precursors. In this study, we present a theory-guided synthesis of a tungsten-based MXene, W2TiC2Tx, derived from a non-MAX nanolaminated ternary carbide (W,Ti)4C4−y precursor by the selective etching of one of the covalently bonded tungsten layers. Our results indicate the importance of tungsten and titanium ordering, the presence of vacancy defects in the metal layers, and the lack of oxygen impurities in the carbon layers for the successful selective etching of the precursor. We confirm the atomistic out-of-plane ordering of tungsten and titanium using computational and experimental characterizations. The tungsten-rich basal plane endows W2TiC2Tx MXene with a high electrocatalytic hydrogen evolution reaction performance (∼144 mV overpotential at 10 mA cm−2). This study reports a tungsten-based MXene synthesized from a covalently bonded non-MAX precursor, adding to the synthetic strategies for 2D materials. 

Natural language processing (NLP) has gained widespread adoption in the development of real-world applications. However, the black-box nature of neural networks in NLP applications poses a challenge when evaluating their performance, let alone ensuring it. Recent research has proposed testing techniques to enhance the trustworthiness of NLP-based applications. However, most existing works use a single, aggregated metric (i.e., accuracy) which is difficult for users to assess NLP model performance on fine-grained aspects, such as LCs. To address this limitation, we present ALiCT, an automated testing technique for validating NLP applications based on their LCs. ALiCT takes user-specified LCs as inputs and produces diverse test suite with test oracles for each of given LC. We evaluate ALiCT on two widely adopted NLP tasks, sentiment analysis and hate speech detection, in terms of diversity, effectiveness, and consistency. Using Self-BLEU and syntactic diversity metrics, our findings reveal that ALiCT generates test cases that are 190% and 2213% more diverse in semantics and syntax, respectively, compared to those produced by state-of-the-art techniques. In addition, ALiCT is capable of producing a larger number of NLP model failures in 22 out of 25 LCs over the two NLP applications. 

Abstract It is projected that 10 million deaths could be attributed to drug-resistant bacteria infections in 2050. To address this concern, identifying new-generation antibiotics is an effective way. Antimicrobial peptides (AMPs), a class of innate immune effectors, have received significant attention for their capacity to eliminate drug-resistant pathogens, including viruses, bacteria, and fungi. Recent years have witnessed widespread applications of computational methods especially machine learning (ML) and deep learning (DL) for discovering AMPs. However, existing methods only use features including compositional, physiochemical, and structural properties of peptides, which cannot fully capture sequence information from AMPs. Here, we present SAMP, an ensemble random projection (RP) based computational model that leverages a new type of feature called proportionalized split amino acid composition (PSAAC) in addition to conventional sequence-based features for AMP prediction. With this new feature set, SAMP captures the residue patterns like sorting signals at both the N-terminal and the C-terminal, while also retaining the sequence order information from the middle peptide fragments. Benchmarking tests on different balanced and imbalanced datasets demonstrate that SAMP consistently outperforms existing state-of-the-art methods, such as iAMPpred and AMPScanner V2, in terms of accuracy, Matthews correlation coefficient (MCC), G-measure, and F1-score. In addition, by leveraging an ensemble RP architecture, SAMP is scalable to processing large-scale AMP identification with further performance improvement, compared to those models without RP. To facilitate the use of SAMP, we have developed a Python package that is freely available at https://github.com/wan-mlab/SAMP. 

Deep learning-based code generation (DL-CG) applications have shown great potential for assisting developers in programming with human-competitive accuracy. However, lacking transparency in such applications due to the uninterpretable nature of deep learning models makes the automatically generated programs untrustworthy. In this paper, we develop DeciX, a first explanation method dedicated to DL-CG applications. DeciX is motivated by observing two unique properties of DL-CG applications: output-to-output dependencies and irrelevant value and semantic space. These properties violate the fundamental assumptions made in existing explainable DL techniques and thus cause applying existing techniques to DL-CG applications rather pessimistic and even incorrect. DeciX addresses these two limitations by constructing a causal inference dependency graph, containing a novel method leveraging causal inference that can accurately quantify the contribution of each dependency edge in the graph to the end prediction result. Proved by extensive experiments assessing popular, widely-used DL-CG applications and several baseline methods, DeciX is able to achieve significantly better performance compared to state-of-the-art in terms of several critical performance metrics, including correctness, succinctness, stability, and overhead. Furthermore, DeciX can be applied to practical scenarios since it does not require any knowledge of the DL-CG model under explanation. We have also conducted case studies that demonstrate the applicability of DeciX in practice.