Applying Deep Learning to Answer Selection: A Study and An Open Task

We apply a general deep learning framework to address the non-factoid question answering task. Our approach does not rely on any linguistic tools and can be applied to different languages or domains. Various architectures are presented and compared. We create and release a QA corpus and setup a new QA task in the insurance domain. Experimental results demonstrate superior performance compared to the baseline methods and various technologies give further improvements. For this highly challenging task, the top-1 accuracy can reach up to 65.3% on a test set, which indicates a great potential for practical use.Comment: To appear in the proceedings of ASRU 201

Fourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator

The transformer model is known to be computationally demanding, and prohibitively costly for long sequences, as the self-attention module uses a quadratic time and space complexity with respect to sequence length. Many researchers have focused on designing new forms of self-attention or introducing new parameters to overcome this limitation, however a large portion of them prohibits the model to inherit weights from large pretrained models. In this work, the transformer's inefficiency has been taken care of from another perspective. We propose Fourier Transformer, a simple yet effective approach by progressively removing redundancies in hidden sequence using the ready-made Fast Fourier Transform (FFT) operator to perform Discrete Cosine Transformation (DCT). Fourier Transformer is able to significantly reduce computational costs while retain the ability to inherit from various large pretrained models. Experiments show that our model achieves state-of-the-art performances among all transformer-based models on the long-range modeling benchmark LRA with significant improvement in both speed and space. For generative seq-to-seq tasks including CNN/DailyMail and ELI5, by inheriting the BART weights our model outperforms the standard BART and other efficient models. \footnote{Our code is publicly available at \url{https://github.com/LUMIA-Group/FourierTransformer}

Enhanced Refractive Index Sensor Using a Combination of a Long Period Fiber Grating and a Small Core Singlemode Fiber Structure

An enhanced refractive index (RI) sensor based on a combination of a long period fiber grating (LPG) and a small core singlemode fiber (SCSMF) structure is proposed and developed. Since the LPG and SCSMF transmission spectra experience a blue and a red shift respectively as the surrounding RI (SRI) increases, the sensitivity is improved by measuring the separation between the resonant wavelengths of the LPG and SCSMF structures. Experimental results show that the sensor has a sensitivity of 1028 nm/SRI unit in the SRI range from 1.422 to 1.429, which is higher than individual sensitivities of either structure alone used in the experiment. Experimental results agree well with simulation results

Pure-Silica-Zeolite MFI and MEL Low-Dielectric-Constant Films with Fluoro-Organic Functionalization

The synthesis of organic-functionalized pure-silica-zeolites (PSZs) with MFI- and MEL-type structures for low-k applications prepared through a direct-synthesis method by adding a fluorinated silane to the synthesis solution is reported. The added fluorine functionality increases the hydrophobicity of the zeolites, which are characterized by scanning electron microscopy, X-ray diffraction, 29Si and 19F solid-state NMR spectroscopy, nitrogen adsorption, and thermogravimetric analysis. The functionalized zeolite powders have low water content and calcined spin-on films prepared from the functionalized nanoparticle suspensions exhibit higher water contact angles and lower k values (2.1 and 1.8 for the functionalized MFI- and MEL-type zeolites, respectively) than PSZ films. The use of a direct-synthesis method to decrease the moisture adsorption in the films eliminates the extra post-spin-on silylation steps that are traditionally used to render the zeolite films hydrophobic

Preparation of sea buckthorn (Hippophae rhamnoides L.) seed meal peptide by mixed fermentation and its effect on volatile compounds and hypoglycemia

This study employed mixed bacterial strains to ferment seabuckthorn seed meal into peptides, and conducted a comprehensive evaluation of the growth adaptive conditions, molecular weight distribution, volatile compounds, and in vitro hypoglycemic activity required for fermentation. Results showed that when the amount of maltose was 1.1% and MgSO4·7H2O was added at 0.15 g/L, the peptide yield reached 43.85% with a mixed fermentation of Lactobacillus fermentum, Bacillus subtilis, Lactobacillus casei, Lactobacillus rhamnosus, and Lactobacillus acidophilus. Components with a molecular weight below 1 kDa were found to be more effective in inhibiting the activity of α-amylase and α-glucosidase, with the identified sequence being FYLPKM. Finally, SPME/GC–MS results showed that 86 volatile components were detected during the fermentation of seabuckthorn seed meal, including 22 alcohols, 9 acids, 7 ketones, 14 alkanes, 20 esters, and 14 other compounds. With prolonged fermentation time, the content of acids and esters increased significantly

A multimechanistic antibody targeting receptor-binding sites potently cross-protects against influenza B viruses

流感病毒HA是研制流感药物和流感疫苗的重要靶标，但HA具有高度变异性，如何在高变异HA中找到不变之处，即高度保守表位，是研制流感特效药物和广谱疫苗的关键。近年来国外报道的流感HA广谱中和单抗的识别位点均在较为保守的HA茎部区，而针对流感病毒与细胞受体结合部位的HA头部区尤其是RBS区，一直未能发现广谱中和抗体。夏宁邵教授团队通过探索多种免疫策略和筛选策略，成功筛选出一株广谱中和单抗12G6，识别一个位于HA头部RBS上的全新保守性表位。体外实验显示12G6人源化改造的C12G6抗体能高效中和1940-2016年间世界各地历年流行的代表三个遗传变异亚系的18个乙型流感病毒代表株对细胞的感染，并能保护小鼠致死性感染，治疗效果显著优于已报道的代表性抗体以及抗流感药物；C12G6与“达菲”联合用药具有明显的协同效果。此外，雪貂感染模型的预防和治疗效果进一步证实了C12G6作为抗体药物的治疗潜能。研究还显示该表位是病毒感染复制的关键表位，该位点的突变会造成病毒毒力显著下降。最后，研究揭示了C12G6通过五种不同的抗病毒作用机制发挥作用，提示其高效的抗病毒活性得益于多机制协同效应，这也是目前国内外第一次发现一个流感抗体能通过如此全面的抗病毒机制发挥作用。 该发现为研制能抵抗各种变异株的乙型流感特效治疗药物和通用疫苗带来新希望。 该研究工作依托分子疫苗学和分子诊断学国家重点实验室（厦门大学）、国家传染病诊断试剂与疫苗工程技术研究中心、厦门大学养生堂生物药物联合实验室完成。陈毅歆副教授、夏宁邵教授为该研究论文的共同通讯作者。在读博士研究生沈晨光、陈俊煜、李睿、王国松和硕士研究生张梦娅等为共同第一作者。【Abstract】Influenza B virus causes considerable disease burden worldwide annually, highlighting the limitations of current influenza vaccines and antiviral drugs. In recent years, broadly neutralizing antibodies (bnAbs) against hemagglutinin (HA) have emerged as a new approach for combating influenza. We describe the generation and characterization of a chimeric monoclonal antibody, C12G6, that cross-neutralizes representative viruses spanning the 76 years of influenza B antigenic evolution since 1940, including viruses belonging to the Yamagata, Victoria, and earlier lineages. Notably, C12G6 exhibits broad cross-lineage hemagglutination inhibition activity against influenza B viruses and has higher potency and breadth of neutralization when compared to four previously reported influenza B bnAbs. In vivo, C12G6 confers stronger cross-protection against Yamagata and Victoria lineages of influenza B viruses in mice and ferrets than other bnAbs or the anti-influenza drug oseltamivir and has an additive antiviral effect when administered in combination with oseltamivir. Epitope mapping indicated that C12G6 targets a conserved epitope that overlaps with the receptor binding site in the HA region of influenza B virus, indicating why it neutralizes virus so potently. Mechanistic analyses revealed that C12G6 inhibits influenza B viruses via multiple mechanisms, including preventing viral entry, egress, and HA-mediated membrane fusion and triggering antibody-dependent cell-mediated cytotoxicity and complement-dependent cytotoxicity responses. C12G6 is therefore a promising candidate for the development of prophylactics or therapeutics against influenza B infection and may inform the design of a truly universal influenza vaccine.This research was supported by grants from the National Natural Science Foundation of China (31670934 and 81371817), the Ministry of Science and Technology of the People’s Republic of China (2011ZX09102-009-12 and 2012DFH30020), the Research Grants Council of the Hong Kong Special Administrative Region (7629/13M, 17103214, and 17154516), and a sponsored research agreement from Sanofi Pasteur. 研究工作得到了香港大学新发传染病国家重点实验室和赛诺菲巴斯德公司的技术支持和帮助，获得国家自然科学基金、新药创制国家科技重大专项、科技部对港科技合作项目等课题资助