Theultra-compactbiosensorbasedonthetwo-dimensional(2D)photoniccrystal(PhC)microcavityisinvestigated.TheperformancesofthesensorareanalyzedtheoreticallyusingtheFabry-Perot(F-P)cavitymodelandsimulatedusingthefinite-differencetime-domain(FDTD)method.Thesimulationres

GloVeisanunsupervisedlearningalgorithmforobtainingvectorrepresentationsforwords.Trainingisperformedonaggregatedglobalword-wordco-occurrencestatisticsfromacorpus,andtheresultingrepresentationsshowcaseinterestinglinearsubstructuresofthewordvectorspace.

Differenttypesoffemtosecondopticaltweezershavebecomeapowerfultoolinthemodernbiologicalfield.However,howtocontroltheirregulartargets,includingbiologicalcells,usingfemtosecondopticaltweezersremainstobeexplored.Inthisstudy,humanredbloodcells(hRBCs)aremanipulatedwithfemtosecondopticaltweezers,andtheirstatesunderdifferentlaserpowersareinvestigated.TheresultsindicatethatopticalpotentialtrapsonlycancapturetheedgeofhRBCsunderthelas

Weexperimentallyandtheoreticallyinvestigatedtheperformanceofafiber-opticbasedFourier-domaincommon-pathopticalcoherencetomography(OCT).Thefiber-opticcommon-pathOCToperatedatthe840-nmcenterwavelength.Theresolutionofthesystemwas8.8\mum(inair)andtheworkingdepthus

Amethodofestimatingtherelativeclocksbetweentwospaceborneglobalpositioning.system(GPS)receiversbasedonthesingle-difference(SD)observationsisinvestigatedin.thispaper.Especially,theadvantagesofintroducingadouble-difference(DD)solution.constraint,includingtheorbitsandambiguities,arediscussedwiththesimulateddata.andtherealdataofGravityRecoveryAndClimateExperiment(GRACE)satellites.The.theoreticalaccuracyanalysisshowsthattheaccuracyofthere

Thebasicpropagationpropertiesofthesilicaandsiliconsubwavelength-diameterhollowwirewaveguideshavebeeninvestigatedbycomparison.Itshowsthatthesilicaandsiliconsubwavelength-diameterhollowwirewaveguideshavesomeinterestingproperties,suchasenhancedevanescentfieldinthecladding,enhancedintensityinthehollowcore,andlargewaveguidedispersion.Forthedifferentconfinementability,theenhancedfieldinthehollowcoreandcladdingofthesilicasubwavelength-

PBAN-PyTorch工作的实施。
要求PyTorch＞=0.4.0NumPy＞=1.13.3Python3.6GloVe预先训练的单词向量：下载预训练的单词向量。
将和到\glove\文件夹中。
数据集基于的餐厅和笔记本电脑数据集。
餐厅数据集极性＃积极的＃消极的＃中性的火车2164807637测试728196196笔记本电脑数据集极性＃积极的＃消极的＃中性的火车994870464测试341128169用法训练模型：pythontrain.py--model_namepban--datasetrestaurant显示帮助消息并退出：pythontrain.py-h实施模型LSTM唐杜玉等。
“有效的LSTM，用于目标依赖的情感分类。
”2016

本书是EricEvans对于他自己写的《规模驱动方案-软件中间繁杂性应答之道》的一本字典式的参考书，可用于快捷查找《规模驱动方案》中的诸多不雅点及其扼要评释。
书是英文版的，2015年3月修订版，我已经加了目录，便捷巨匠参阅。
AcknowledgementsDefinitionsPatternLanguageOverviewI.PuttingtheModeltoWorkBoundedContextUbiquitousLanguageContinuousIntegrationModel-DrivenDesignHands-onModelersRefactoringTowardDeeperInsightII.BuildingBlocksofaModel-DrivenDesignLayeredArchitectureEntitiesValueObjectsDomainEvents*ServicesModulesAggregatesRepositoriesFactoriesIII.SuppleDesignIntention-RevealingInterfacesSide-Effect-FreeFunctionsAssertionsStandaloneClassesClosureofOperationsDeclarativeDesigniiiDrawingonEstablishedFormalismsConceptualContoursIV.ContextMappingforStrategicDesignContextMapPartnership*SharedKernelCustomer/SupplierDevelopmentConformistAnticorruptionLayerOpen-hostServicePublishedLanguageSeparateWaysBigBallofMud*V.DistillationforStrategicDesignCoreDomainGenericSubdomainsDomainVisionStatementHighlightedCoreCohesiveMechanismsSegregatedCoreAbstractCoreVI.Large-scaleStructureforStrategicDesignEvolvingOrderSystemMetaphorResponsibilityLayersKnowledgeLevelPluggableComponentFramework

GRU（GatedRecurrentUnit）神经网络是LSTM的一个变体，GRU在保持了LSTM的效果同时又使结构愈加简单，是一种非常流行RNN神经网络，它只有两个门了，分别为更新门tz和重置门tr。
更新门控制前一时刻的状态信息被带入到当前状态中的程度，值越大前一时刻的状态信息带入越多。
重置门控制忽略前一时刻的状态信息的程度，值越小说明忽略得越多。

GloVeisanunsupervisedlearningalgorithmforobtainingvectorrepresentationsforwords.Trainingisperformedonaggregatedglobalword-wordco-occurrencestatisticsfromacorpus,andtheresultingrepresentationsshowcaseinterestinglinearsubstructuresofthewordvectorspace.

在日常工作中，钉钉打卡成了我生活中不可或缺的一部分。然而，有时候这个看似简单的任务却给我带来了不少烦恼。 每天早晚，我总是得牢记打开钉钉应用，点击"工作台"，再找到"考勤打卡"进行签到。有时候因为工作忙碌，会忘记打卡，导致考勤异常，影响当月的工作评价。而且，由于我使用的是苹果手机，有时候系统更新后，钉钉的某些功能会出现异常，使得打卡变得更加麻烦。 另外，我的家人使用的是安卓手机，他们也经常抱怨钉钉打卡的繁琐。尤其是对于那些不太熟悉手机操作的长辈来说，每次打卡都是一次挑战。他们总是担心自己会操作失误，导致打卡失败。 为了解决这些烦恼，我开始思考是否可以通过编写一个全自动化脚本来实现钉钉打卡。经过一段时间的摸索和学习，我终于成功编写出了一个适用于苹果和安卓系统的钉钉打卡脚本。