One Adapter for All Programming Languages? Adapter Tuning for Code Search and Summarization

As pre-trained models automate many code intelligence tasks, a widely used paradigm is to fine-tune a model on the task dataset for each programming language. A recent study reported that multilingual fine-tuning benefits a range of tasks and models. However, we find that multilingual fine-tuning leads to performance degradation on recent models UniXcoder and CodeT5. To alleviate the potentially catastrophic forgetting issue in multilingual models, we fix all pre-trained model parameters, insert the parameter-efficient structure adapter, and fine-tune it. Updating only 0.6\% of the overall parameters compared to full-model fine-tuning for each programming language, adapter tuning yields consistent improvements on code search and summarization tasks, achieving state-of-the-art results. In addition, we experimentally show its effectiveness in cross-lingual and low-resource scenarios. Multilingual fine-tuning with 200 samples per programming language approaches the results fine-tuned with the entire dataset on code summarization. Our experiments on three probing tasks show that adapter tuning significantly outperforms full-model fine-tuning and effectively overcomes catastrophic forgetting.Comment: Accepted to the 45th International Conference on Software Engineering (ICSE 2023

Survey and Visual Detection of Zaire ebolavirus in Clinical Samples Targeting the Nucleoprotein Gene in Sierra Leone

Ebola virus (EBOV) can lead to severe hemorrhagic fever with a high risk of death in humans and other primates. To guide treatment and prevent spread of the viral infection, a rapid and sensitive detection method is required for clinical samples. Here, we described and evaluated a reverse transcription loop-mediated isothermal amplification (RT-LAMP) method to detect Zaire ebolavirus using the nucleoprotein gene (NP) as a target sequence. Two different techniques were used, a calcein/Mn2+ complex chromogenic method and real-time turbidity monitoring. The RT-LAMP assay detected the NP target sequence with a limit of 4.56 copies/μL within 45 min under 61°C, a similar even or increase in sensitivity than that of real-time reverse transcription-polymerase chain reaction (RT-PCR). Additionally, all pseudoviral particles or non- Zaire EBOV genomes were negative for LAMP detection, indicating that the assay was highly specific for EBOV. To appraise the availability of the RT-LAMP method for use in clinical diagnosis of EBOV, of 417 blood or swab samples collected from patients with clinically suspected infections in Sierra Leone, 307 were identified for RT-LAMP-based surveillance of EBOV. Therefore, the highly specific and sensitive RT-LAMP method allows the rapid detection of EBOV, and is a suitable tool for clinical screening, diagnosis, and primary quarantine purposes

Chlorogenic Acid Ameliorates Damage Induced by Fluorene-9-Bisphenol in Porcine Sertoli Cells

4,4′-(9-Fluorenylidene) diphenol (BPFL, also known as BHPF and fluorene-9-bisphenol) is a novel bisphenol A substitute that is used in the plastics industry as an organic synthesis intermediate and is a potential endocrine disruptor. However, the deleterious effects of BPFL on porcine Sertoli cells (SCs) and the possible underlying mechanisms are still unclear. Chlorogenic acid (CA) is a free radical scavenger in the cellular antioxidant system that prevents oxidative damage and apoptosis. In the present research, we found that BPFL induced impairments in porcine SCs in a dose-dependent manner and that CA protected porcine SCs against BPFL exposure-induced impairments. Cell viability, proliferation and apoptosis assay results revealed that BPFL exposure could inhibit porcine SC proliferation and induce apoptosis, while CA supplementation ameliorated the effects of BPFL. Further analysis revealed that BPFL exposure induced oxidative stress, mitochondrial membrane potential dysfunction and DNA damage accumulation. Transcriptome analysis and further real-time quantitative PCR and Western blot results showed that BPFL exposure induced endoplasmic reticulum stress and apoptosis. Supplementation with CA dramatically ameliorated these phenotypes in BPFL-exposed porcine SCs. Overall, the present research reveals the possible underlying mechanisms by which BPFL exposure induced impairments and CA supplementation protected against these impairments in porcine SCs

Engineering grain boundaries at the 2D limit for the hydrogen evolution reaction

Atom-thin transition metal dichalcogenides (TMDs) have emerged as fascinating materials and key structures for electrocatalysis. So far, their edges, dopant heteroatoms and defects have been intensively explored as active sites for the hydrogen evolution reaction (HER) to split water. However, grain boundaries (GBs), a key type of defects in TMDs, have been overlooked due to their low density and large structural variations. Here, we demonstrate the synthesis of wafer-size atom-thin TMD films with an ultra-high-density of GBs, up to ~1012 cm−2. We propose a climb and drive 0D/2D interaction to explain the underlying growth mechanism. The electrocatalytic activity of the nanograin film is comprehensively examined by micro-electrochemical measurements, showing an excellent hydrogen-evolution performance (onset potential: −25 mV and Tafel slope: 54 mV dec−1), thus indicating an intrinsically high activation of the TMD GBs

Analysis of DNA Methylation in Various Swine Tissues

DNA methylation is known to play an important role in regulating gene expression during biological development and tissue differentiation in eukaryotes. In this study, we used the fluorescence-labeled methylation-sensitive amplified polymorphism (F-MSAP) method to assess the extent and pattern of cytosine methylation in muscle, heart, liver, spleen, lung, kidney and stomach from the swine strain Laiwu, and we also examined specific methylation patterns in the seven tissues. In total, 96,371 fragments, each representing a recognition site cleaved by either or both EcoRI + HpaII and EcoRI + MspI, the HpaII and MspI are isoschizomeric enzymes, were amplified using 16 pairs of selective primers. A total of 50,094 sites were found to be methylated at cytosines in seven tissues. The incidence of DNA methylation was approximately 53.99% in muscle, 51.24% in the heart, 50.18% in the liver, 53.31% in the spleen, 51.97% in the lung, 51.15% in the kidney and 53.39% in the stomach, as revealed by the incidence of differential digestion. Additionally, differences in DNA methylation levels imply that such variations may be related to specific gene expression during tissue differentiation, growth and development. Three types of bands were generated in the F-MSAP profile, the total numbers of these three types of bands in the seven tissues were 46,277, 24,801 and 25,293, respectively

A Method for Multidisciplinary System Analysis Based on Minimal Feedback Variables

As modern engineering design usually involves dependence of one discipline on another, multidisciplinary system analysis (MDSA) plays an important role in the multidisciplinary simulation and design optimization on coupled systems. The paper proposes an MDSA method based on minimal feedback variables (MDSA_MF) to enhance the solving efficiency. There are two phases in the method. In phase 1, design structural matrix (DSM) is introduced to represent a coupled system, and each off-diagonal element is denoted by a coupling variable set; then an optimal sequence model is built to obtain a reordered DSM with minimal number of feedback variables. In phase 2, the feedback in the reordered DSM is broken, so that the coupled system is transformed into one directed acyclic graph; then, regarding the inputs depending on the broken feedback as independent variables, a least-squares problem is constructed to minimize the residuals of these independents and corresponding outputs to zero, which means the multidisciplinary consistence is achieved. Besides, the MDSA_MF method is implemented in a multidisciplinary platform called FlowComputer. Several examples of coupled systems are modeled and solved in the platform using several MDSA methods. The results demonstrate that the proposed method could enhance the solving efficiency of coupled systems

Efficient Cluster-Based k-Nearest-Neighbor Machine Translation

k-Nearest-Neighbor Machine Translation (kNN-MT) has been recently proposed as a non-parametric solution for domain adaptation in neural machine translation (NMT). It aims to alleviate the performance degradation of advanced MT systems in translating out-of-domain sentences by coordinating with an additional token-level feature-based retrieval module constructed from in-domain data. Previous studies have already demonstrated that non-parametric NMT is even superior to models fine-tuned on out-of-domain data. In spite of this success, kNN retrieval is at the expense of high latency, in particular for large datastores. To make it practical, in this paper, we explore a more efficient kNN-MT and propose to use clustering to improve the retrieval efficiency. Concretely, we first propose a cluster-based Compact Network for feature reduction in a contrastive learning manner to compress context features into 90+% lower dimensional vectors. We then suggest a cluster-based pruning solution to filter out 10%-40% redundant nodes in large datastores while retaining translation quality. Our proposed methods achieve better or comparable performance while reducing up to 57% inference latency against the advanced non-parametric MT model on several machine translation benchmarks. Experimental results indicate that the proposed methods maintain the most useful information of the original datastore and the Compact Network shows good generalization on unseen domains.Comment: 8 pages,6 figures, Accepted by ACL 2022 main conferenc