Probabilistic learning on manifolds (PLoM) with partition

The probabilistic learning on manifolds (PLoM) introduced in 2016 has solved difficult supervised problems for the “small data” limit where the number N of points in the training set is small. Many extensions have since been proposed, making it possible to deal with increasingly complex cases. However, the performance limit has been observed and explained for applications for which N is very small and for which the dimension of the diffusion-map basis is close to N. For these cases, we propose a novel extension based on the introduction of a partition in independent random vectors. We take advantage of this development to present improvements of the PLoM such as a simplified algorithm for constructing the diffusion-map basis and a new mathematical result for quantifying the concentration of the probability measure in terms of a probability upper bound. The analysis of the efficiency of this extension is presented through two applications.     

机器翻译译文质量估计综述

机器翻译译文质量估计(Quality Estimation,QE)是指在不需要人工参考译文的条件下,估计机器翻译系统产生的译文的质量,对机器翻译研究和应用具有很重要的价值。机器翻译译文质量估计经过最近几年的发展,取得了丰富的研究成果。该文首先介绍了机器翻译译文质量估计的背景与意义;然后详细介绍了句子级QE、单词级QE、文档级QE的具体任务目标、评价指标等内容,进一步概括了QE方法发展的三个阶段: 基于特征工程和机器学习的QE方法阶段,基于深度学习的QE方法阶段,融入预训练模型的QE方法阶段,并介绍了每一阶段中的代表性研究工作;最后分析了目前的研究现状及不足,并对未来QE方法的研究及发展方向进行了展望。     

快速近似计算Shapley值的归因解释方法

Shapley值归因解释方法虽然能更准确量化解释结果, 但过高的计算复杂度严重影响了该方法的实用性. 本文引入KD树重新整理待解释模型的预测数据, 通过在KD树上插入虚节点, 使之满足TreeSHAP算法的使用条件, 在此基础上提出了KDSHAP方法. 该方法解除了TreeSHAP算法仅能解释树结构模型的限制, 将该算法计算Shapley值的高效性放宽到对所有的黑盒模型的解释中, 同时保证了计算准确度. 通过实验对比分析, KDSHAP方法的可靠性, 以及在解释高维输入模型时的适用性.     

Automated prediction system of household energy consumption in cities using web crawler and optimized artificial intelligence

The supply of electrical energy is critical to convenient and comfortable living. However, people consume a large amount of energy, contributing to an energy crisis and global warming, and damaging some ecological cycles. Residential electricity consumption has greater elasticity than industrial and business consumption; it therefore has high energy-saving potential. This work establishes an automated platform, which provides information about residential electricity consumption in each city in Taiwan. Machine learning was used to forecast future residential electricity demand. A nature-inspired optimization method was applied to enhance the accuracy of the best machine learner, yielding an even better hybrid ensemble model. Performance measures indicate that the resulting model is accurate and provides effective information for reference. An automatic web-based system based on the model was combined with a web crawler and scheduled to run automatically to provide information on monthly residential electricity consumption in each county and city. By providing energy consumption information across the country, power providers and government can discuss policy and set different goals for energy use. The results of this study can facilitate the early implementation of energy-saving and carbon emission-reducing in cities and aid utility companies in establishing energy conservation guidelines.     

Prediction of fish quality level with machine learning

In this study, sea bream, sea bass, anchovy and trout were captured and recorded using a digital camera during refrigerated storage for 7 days. In addition, their total viable counts (TVC) were determined on a daily basis. Based on the TVC, each fish was classified as ‘fresh’ when it was <5 log cfu per g, and as ‘not fresh’ when it was >7 log cfu per g. They were uploaded on a web-based machine learning software called Teachable Machine (TM), which was trained about the pupils and heads of the fish. In addition, images of each species from different angles were uploaded to the software in order to ensure the recognition of fish species by TM. The data of the study indicated that the TM was able to distinguish fish species with high accuracy rates and achieved over 86% success in estimating the freshness of the fish species tested.     

具有保护肠上皮细胞HT-29免受大肠杆菌和H2O2损伤潜力的植物乳杆菌的筛选

益生菌可在肠道定植从而发挥抗炎或抗氧化活性，有利于宿主肠道健康。本实验研究了从新疆传统发酵乳制品中分离得到的8?株植物乳杆菌对大肠杆菌侵袭和过氧化氢刺激肠上皮细胞HT-29的保护作用。结果表明：在8?株植物乳杆菌中，植物乳杆菌35具有最高的黏附能力。植物乳杆菌35可通过取代、竞争、排阻的方式抑制大肠杆菌对HT-29细胞的黏附，抑制率分别为42.60%、59.17%、60.19%。植物乳杆菌35及其多糖可抑制大肠杆菌刺激HT-29细胞产生白细胞介素-8；同时保护HT-29细胞免受过氧化氢的损伤，增加超氧化物歧化酶、谷胱甘肽过氧化物酶活力水平并降低丙二醛含量。结论：植物乳杆菌35及其粗胞外多糖具有抑制大肠杆菌O157诱导的炎症性肠病的潜力。     

面向小语种机器翻译的平行语料库构建方法

神经机器翻译模型的训练效果在很大程度上取决于平行语料库的规模和质量。除了一些常见语言外,汉语与小语种间高质量平行语料库的建设一直处于滞后状态。现有小语种平行语料库多采用自动句子对齐技术利用网络资源构建而成,在文本质量和领域等方面有诸多局限性。采用人工翻译的方式可以构建高质量平行语料库,但是缺乏相关经验和方法。文中从机器翻译实践者和研究者角度出发,介绍了经济高效的人工构建小语种平行语料库的工作,包括其总体目标、实施过程、流程细节和最后结果。在构建过程中尝试并积累了各种经验,形成了小语种到汉语平行语料库构建方法、建议的总结。最终,成功构建了波斯语到汉语、印地语到汉语、印度尼西亚语到汉语各50万条高质量平行语料。实验结果表明,所构建的平行语料库有较好的质量,提高了小语种神经机器翻译模型的训练效果。     

四氢姜黄素经PI3K/Akt信号通路对人血小板活化和聚集的调控作用

目的：探讨姜黄素的主要肠道代谢物四氢姜黄素（tetrahydrocurcumin，THC）对血小板活化和聚集的影响及其可能的分子机制。方法：在体外实验中，用不同浓度的THC（0、0.5、1、10 μmol/L）提前与健康人纯化血小板共同孵育40 min，然后加入凝血酶激活血小板2 min，用流式细胞术测定血小板表面CD62P和CD63的表达量，用酶联免疫吸附法测定血小板释放血小板因子-4（platelet factor-4，PF4）和趋化因子配体-5（chemokine ligand 5，CCL5）水平，用血小板聚集仪检测血小板释放ATP水平和血小板最大聚集率，用Western blot蛋白免疫印迹法检测血小板磷酸肌醇-3-激酶（phosphoinositide 3-kinase，PI3K）和Akt蛋白的磷酸化水平。结果：与模型组（血小板悬液中加入0.05%二甲基亚砜）相比，THC能抑制凝血酶诱导的血小板表面CD62P和CD63的表达，抑制PF4、CCL5和ATP的释放，降低血小板最大聚集率，下调PI3K和Akt蛋白的磷酸化水平，且呈浓度依赖效应，其中10 μmol/L的浓度下作用效果显著（P＜0.01、P＜0.001）。PI3K的特异性激动剂740 Y-P可部分逆转THC对PF4和CCL5释放和血小板聚集的抑制作用（P＜0.05、P＜0.01）。结论：THC具有显著抑制血小板活化和聚集的作用，其机制可能是THC可下调PI3K/Akt介导的信号通路。     

智能数据分区与布局研究

大数据时代,数据规模庞大,由数据进行驱动的应用分析场景日益增多.如何快速、高效地从这些海量数据中提取出用以分析决策的信息,给数据库系统带来重大挑战.同时,现代商业分析决策对分析数据的实时性要求数据库系统能够同时快速处理ACID事务和复杂的分析查询.然而,传统的数据分区粒度太粗,且不能适应动态变化的复杂分析负载;传统的数据布局单一,不能应对现代大量增加的混合事务分析应用场景.为了解决以上问题,“智能数据分区与布局”成为当前的研究热点之一,它通过数据挖掘、机器学习等技术抽取工作负载的有效特征,设计最佳的分区策略来避免扫描大量不相关的数据,指导布局结构设计以适应不同类型的工作负载.首先介绍了智能数据分区与布局的相关背景知识,然后对智能数据分区与布局技术的研究动机、发展趋势、关键技术进行详细的阐述.最后,对智能数据分区与布局技术的研究前景做出总结与展望.