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[{"text": "【全球最大的学术组织#IEEE禁止华为系审稿人#北大教授公开抗议】5月29消息，媒体报道IEEE通过邮件宣布华为员工将被禁止作为旗下期刊杂志的编辑和审稿人。IEEENTC北京分会主席、北京大学教授张海霞在接受记者采访时确认她的多个朋友收到了IEEE发的邮件。同时张海霞教授发表公开声明，表示“这远远超出了一个可以接受学术人的底线，做为IEEE的会员和期刊编委，我必须表明自己的态度：我申请退出我所在的两个IEEE期刊的编委会，并给IEEE候任主席写了一封公开信。这是我个人的态度，与任何其他组织和个人无关，特此声明。”（@中国日报）#北大教授退出IEEE编委##IEEE下令清理华为系审稿人##北大教授回应退出IEEE编委#", "author": "环球网", "time": "环球网2019年05月29日20:23 来自微博weibo.com", "transmit": 581, "comment": 368, "zan": 2482, "sentiment": [{"positive_prob": 0.231886, "confidence": 0.484697, "negative_prob": 0.768114, "sentiment": 0}]}, {"text": "#北大教授申请退出IEEE编委会#【北大教授张海霞致IEEE主席公开信：申请退出编委会】北大教授张海霞致IEEE主席公开信：今日惊闻IEEE下令禁止华为专家参与期刊审稿，这远远超出了一个可以接受学术人的底线，做为IEEE的会员和期刊编委，我必须表明自己的态度：我申请退出我所在的两个IEEE期刊的编委会，并给IEEE候任主席写了一封公开信。这是我个人态度，与任何其他组织和个人无关。此前，全球最大学术组织对华为禁令曝光，邮件中禁止华为员工作为旗下期刊杂志的编辑和审稿人。#IEEE下令清理华为系审稿人#", "author": "钛媒体APP", "time": "钛媒体APP2019年05月29日19:04 来自微博weibo.com", "transmit": 1806, "comment": 455, "zan": 10479, "sentiment": [{"positive_prob": 0.0971239, "confidence": 0.784169, "negative_prob": 0.902876, "sentiment": 0}]}, {"text": "【#北大教授退出IEEE编委#：禁令超出所有人的底线！】5月29日，IEEE（国际电气与电子工程师协会）发内部邮件下令禁止华为专家参与期刊审稿，北大教授张海霞怒发公开信，请辞IEEE编委一职。她表示，这个声明超出科学家、学术人的基本底线。“为什么要去做学术、为什么要参加学术组织？就是因为相信我们是为科学工作，公开、公正、公平是最基本的原则，而这个声明让我们感觉到我们受到严重的挑战。不管是谁，如果他符合去审稿的标准，我都可以邀请他，哪怕这是我的对手，哪怕他是我的敌人，但是绝对不可以，因为他来自哪里，我就我就把他排除在外，这是一个基本的学术原则”。@梨视频文化L梨视频文化的秒拍视频", "author": "新浪视频", "time": "新浪视频2019年05月29日21:13 来自微博weibo.com", "transmit": 793, "comment": 364, "zan": 3715, "sentiment": [{"positive_prob": 0.0616853, "confidence": 0.862922, "negative_prob": 0.938315, "sentiment": 0}]}, {"text": "【全球最大的学术组织#IEEE禁止华为系审稿人##北大教授退出IEEE编委#：禁令超出所有人的底线！】5月29消息，媒体报道IEEE通过邮件宣布华为员工将被禁止作为旗下期刊杂志的编辑和审稿人。IEEENTC北京分会主席、北京大学教授张海霞在接受中国日报记者采访时确认她的多个朋友收到了IEEE发的邮件。同时张海霞教授发表公开声明，表示“这远远超出了一个可以接受学术人的底线，做为IEEE的会员和期刊编委，我必须表明自己的态度：我申请退出我所在的两个IEEE期刊的编委会，并给IEEE候任主席写了一封公开信。这是我个人的态度，与任何其他组织和个人无关，特此声明。”O全球最大的学术组织IEEE禁止华为系审稿人北大教授公开抗议（视频来源：@梨视频文化）L梨视频文化的秒拍视频", "author": "每日经济新闻", "time": "每日经济新闻2019年05月29日21:46 来自微博weibo.com", "transmit": 148, "comment": 74, "zan": 396, "sentiment": [{"positive_prob": 0.35664, "confidence": 0.207467, "negative_prob": 0.64336, "sentiment": 0}]}, {"text": "【#IEEE下令清理华为系审稿人#，全球最大学术组织禁令邮件曝光】5月29日消息，IEEE被曝对华为说不。IEEE（InstituteofElectricalandElectronicsEngineers，国际电气与电子工程师协会），当今世界电子、电气、计算机、通信、自动化工程技术研究领域最著名、规模最大的非营利性跨国学术组织。其最新曝光的相关邮件要求，禁止华为员工作为旗下期刊杂志的编辑和审稿人。接下来，华为相关的投稿、文章、会议赞助，以及分支协会职务，可能都会受到关联影响。邮件中称，由于美国商务部将华为列入黑名单，IEEE做出如下声明：“根据IEEEFAQ文档第12项，我们不能使用华为员工作为期刊的同行评审过程的审稿人或编辑。如果我们继续这样做，可能会产生严重的法律影响。我们可以让华为员工留在编辑委员会，只是不能处理任何文件，直到该公司从商务部黑名单中删除。我们别无选择，只能遵守这些新规定，强烈建议您执行以下程序：如果您的编辑委员会中有华为的编辑，请首先通知他们，并向他们解释情况。一旦您通知了这些编辑，请向所有编辑发送电子邮件，并告知他们不能再使用华为员工作为审稿人。如果目前有论文被分配给华为员工，请建议他们寻找替代者。请编辑向华为审核人员解释情况。”需要说明的是，华为一直是IEEE的重要支持力量。除了员工担任学术职务，作为技术巨头公司，华为与IEEE也有不少合作。这次IEEE发邮件，对华为说No，对于双方之间的合作，也必然会造成影响。此次突发事件，让不少微博网友的三观崩塌。说好的“科学无国界”呢？（量子位）", "author": "中国企业家杂志", "time": "中国企业家杂志2019年05月29日13:03 来自专业版微博", "transmit": 3548, "comment": 1062, "zan": 4155, "sentiment": [{"positive_prob": 0.0488782, "confidence": 0.891382, "negative_prob": 0.951122, "sentiment": 0}]}, {"text": "【#北大教授退出IEEE编委#，回应称：损伤了所有学术人的基本底线】5月29日，IEEE通过邮件宣布华为员工将被禁止作为旗下期刊杂志的编辑和审稿人。北京大学教授、IEEE编委张海霞发声明申请退出编委会，她回应：IEEE的决定损伤了所有科学家、学术人的基本底线。L老板联播的秒拍视频", "author": "头条新闻", "time": "头条新闻2019年05月29日22:19 来自微博weibo.com", "transmit": 1050, "comment": 766, "zan": 5591, "sentiment": [{"positive_prob": 0.00126756, "confidence": 0.997183, "negative_prob": 0.998732, "sentiment": 0}]}, {"text": "【#IEEE禁止华为员工作为旗下期刊杂志的编辑和审稿人#】近日，IEEE（全球电气与电子工程师协会）发通知禁止华为员工作为旗下期刊杂志的编辑和审稿人。在电子、计算机领域，该组织的期刊发表的文献占据了30%。看来美国这次是要从政治、经济、标准制定组织到学术圈等全方位封锁华为了。#华为##IEEE下令清理华为系审稿人##北大教授退出IEEE编委#", "author": "福布斯中文网", "time": "福布斯中文网2019年05月29日12:28 来自微博weibo.com", "transmit": 1169, "comment": 1345, "zan": 1543, "sentiment": [{"positive_prob": 0.659641, "confidence": 0.243646, "negative_prob": 0.340359, "sentiment": 2}]}, {"text": "#IEEE下令清理华为系审稿人#【华为又被限了，这次是在学术界】5月29日，IEEE的一封邮件，把原本已承受巨大压力的华为再一次推上了风口浪尖。作为目前全球最大的非营利性专业技术学会，IEEE决定遵照美国禁令，在学术论文被IEEE接受前，禁止华为员工、以及华为赞助的个人担任审稿人和编辑。也就是说，“华为人”不能参与初审等前期工作，政治因素的压力已经传导到了学界，这也让学术界的同仁直呼荒谬。详情：O华为又被限了，这次是在学术界", "author": "21世纪经济报道", "time": "21世纪经济报道2019年05月29日16:45 来自360安全浏览器", "transmit": 927, "comment": 2, "zan": 1438, "sentiment": [{"positive_prob": 0.898903, "confidence": 0.77534, "negative_prob": 0.101097, "sentiment": 2}]}, {"text": "【全球最大的学术组织#IEEE下令清理华为系审稿人#，#北大教授退出IEEE编委#抗议】5月29日，IEEE通过邮件宣布华为员工将被禁止作为旗下期刊杂志的编辑和审稿人。IEEENTC北京分会主席、北京大学教授张海霞在接受记者采访时确认她的多个朋友收到了IEEE发的邮件。同时张海霞教授发表公开声明，表示“这远远超出了一个可以接受学术人的底线，做为IEEE的会员和期刊编委，我必须表明自己的态度：我申请退出我所在的两个IEEE期刊的编委会，并给IEEE候任主席写了一封公开信。这是我个人的态度，与任何其他组织和个人无关，特此声明。”L梨视频文化的秒拍视频（梨视频）", "author": "中国新闻周刊", "time": "中国新闻周刊2019年05月29日20:46", "transmit": 168, "comment": 86, "zan": 451, "sentiment": [{"positive_prob": 0.405286, "confidence": 0.0993652, "negative_prob": 0.594714, "sentiment": 0}]}, {"text": "【#北大教授回应退出IEEE编委#：禁令超出所有人的底线！】5月29日，IEEE（国际电气与电子工程师协会）发内部邮件下令禁止华为专家参与期刊审稿，北大教授张海霞怒发公开信，请辞IEEE编委一职。她告诉@梨视频文化，这个声明超出科学家、学术人的基本底线。“为什么要去做学术、为什么要参加学术组织？就是因为相信我们是为科学工作，公开、公正、公平是最基本的原则，而这个声明让我们感觉到我们受到严重的挑战。不管是谁，如果他符合去审稿的标准，我都可以邀请他，哪怕这是我的对手，哪怕他是我的敌人，但是绝对不可以，因为他来自哪里，我就我就把他排除在外，这是一个基本的学术原则”。#北大教授退出IEEE编委#L梨视频文化的秒拍视频", "author": "梨视频", "time": "梨视频2019年05月29日20:31 来自微博weibo.com", "transmit": 336, "comment": 94, "zan": 542, "sentiment": [{"positive_prob": 0.0143154, "confidence": 0.968188, "negative_prob": 0.985685, "sentiment": 0}]}, {"text": "【全球最大的学术组织#IEEE禁止华为系审稿人#北大教授公开抗议】5月29消息，媒体报道IEEE通过邮件宣布华为员工将被禁止作为旗下期刊杂志的编辑和审稿人。IEEENTC北京分会主席、北京大学教授张海霞在接受中国日报记者采访时确认她的多个朋友收到了IEEE发的邮件。同时张海霞教授发表公开声明，表示“这远远超出了一个可以接受学术人的底线，做为IEEE的会员和期刊编委，我必须表明自己的态度：我申请退出我所在的两个IEEE期刊的编委会，并给IEEE候任主席写了一封公开信。这是我个人的态度，与任何其他组织和个人无关，特此声明。”（记者马思邹硕）", "author": "中国日报", "time": "中国日报2019年05月29日19:56 来自搜狗高速浏览器", "transmit": 82, "comment": 73, "zan": 223, "sentiment": [{"positive_prob": 0.174479, "confidence": 0.612268, "negative_prob": 0.825521, "sentiment": 0}]}, {"text": "其实IEEE对于华为的所采取的举措并非欧美学术界对于华为采取相应措施的孤例，从2019年一月起，包括牛津、斯坦福、加州大学伯克利分校、麻省理工学院在内的世界顶级名校都相继暂停或终止了与华为的合作。#IEEE下令清理华为系审稿人#", "author": "知识分子", "time": "知识分子2019年05月29日20:20 来自iPhoneXS", "transmit": 405, "comment": 41, "zan": 187, "sentiment": [{"positive_prob": 0.940826, "confidence": 0.868503, "negative_prob": 0.0591738, "sentiment": 2}]}, {"text": "【#IEEE下令清理华为系审稿人#后续，#北京大学教授辞去IEEE编委会职务#】此前，IEEE（全球电气与电子工程师协会）发通知禁止华为员工作为旗下期刊杂志的编辑和审稿人。此举引起轩然大波，“科学无国界”彷佛成了玩笑。北京大学张海霞教授已致函IEEE辞去编委会职务，表达自己对IEEE此举的抗议。#北大教授退出IEEE编委#", "author": "福布斯中文网", "time": "福布斯中文网2019年05月29日17:31 来自微博weibo.com", "transmit": 564, "comment": 632, "zan": 2486, "sentiment": [{"positive_prob": 0.152218, "confidence": 0.661737, "negative_prob": 0.847782, "sentiment": 0}]}, {"text": "【学术组织IEEE排斥华为，北大清华学者当即抗议】5月29日，网络上曝光的邮件显示，由于美国政府将华为列入“实体清单”，全球顶级学术组织IEEE（电气电子工程师学会）向旗下刊物的主编提出这样的要求：禁止来自华为的同僚担任审稿人或编辑，否则将产生严重的“法律牵连观察者网向IEEE旗下某期刊的一名编委求证，得到了“可信度很高”的回答。此事在学术圈引起轩然大波，接连有北大、清华的教职人员表态称，将与IEEE“断交”。不过，有关此事对华为的影响，一位不愿意透露姓名的美国大学教授向观察者网指出，“物质层面的影响是不大，因为跟华为有关系的编辑审稿人、以及来自华为的文章只占非常小的比例”、华为依旧可以看IEEE的文章、“它作为一家公司，发不发表文章，多一篇少一篇也不是很重要。”但他进一步表示，IEEE这种操作，突破了学术期刊和专业协会（原来大家以为有）的一个底线。“不好的影响主要在于学术的自由性受到了干预。华为如果能够参与IEEE这个工作，对它的形象和学术影响力是有帮助的，但目前这一方面遭受了阻力。”实际上，这个底线也不是第一次被突破了。2003年，IEEE颁布了一项禁令，禁止古巴、伊朗、利比亚和苏丹的居民在IEEE的任何出版物上发表文献、或是对其任何标准做出贡献，而上述四个国家都在美国的“禁运清单”上。O知名学术组织IEEE排斥华为，北大清华学者当即抗议", "author": "观察者网", "time": "观察者网2019年05月29日23:46 来自微博weibo.com", "transmit": 131, "comment": 157, "zan": 332, "sentiment": [{"positive_prob": 0.0157162, "confidence": 0.965075, "negative_prob": 0.984284, "sentiment": 0}]}, {"text": "【学术圈炸了！IEEE限制华为相关专家参与审稿】今日，一封邮件传遍了学术界：IEEE禁止华为的同事担任审稿人或编辑。邮件表明”可以让华为的同事留在我们的编辑委员会，但他们不能参与任何论文的审阅，直到华为从BIS名单中删除。对此，学术界表示深深地担忧，南京大学周志华教授也发布朋友圈表示：赤裸裸的干涉学术，美帝毁尽三观..O学术圈炸了！IEEE限制华为相关专家参与审稿", "author": "TechWeb", "time": "TechWeb2019年05月29日15:13 来自微博weibo.com", "transmit": 123, "comment": 174, "zan": 487, "sentiment": [{"positive_prob": 0.034657, "confidence": 0.922984, "negative_prob": 0.965343, "sentiment": 0}]}, {"text": "【怒退IEEE北大教授：将企业列“黑名单”，全世界的科学家都不能忍】29日，一则“#IEEE下令清理华为系审稿人#”的消息在中国网友中引起广泛关注。消息显示，IEEE在一封邮件中，禁止华为员工作为旗下期刊杂志的编辑和审稿人，拿过华为科研经费的也不能作为审稿人。 邮件被曝光之后，立即在学术圈引起轩然大波。北京大学信息科学技术学院教授张海霞29日公开致信IEEE主席称，此举远超出一个学术人可以接受的底线，“做为IEEE的会员和期刊编委，我必须表明自己的态度：我申请退出我所在的两个IEEE期刊的编委会”。当天，张海霞就此事接受了《环球时报》的专访，专访实录如下↓↓#北大教授退出IEEE编委##对话为华为怒退IEEE的北大教授#O对话为华为怒退IEEE的北大教授:他们侮辱全世界科学家!", "author": "环球网", "time": "环球网2019年05月29日23:53 来自微博weibo.com", "transmit": 78, "comment": 69, "zan": 353, "sentiment": [{"positive_prob": 0.0030511, "confidence": 0.99322, "negative_prob": 0.996949, "sentiment": 0}]}, {"text": "IEEE直接把华为给踢了？？？？？禁止华为员工当他们审稿人或编辑。", "author": "博士圈", "time": "博士圈2019年05月29日11:54 来自会做实验的Android", "transmit": 514, "comment": 134, "zan": 614, "sentiment": [{"positive_prob": 8.3866e-06, "confidence": 0.999981, "negative_prob": 0.999992, "sentiment": 0}]}, {"text": "《学术自由，华为除外！特朗普又给我们上了一课》全球最大学术组织IEEE拉黑华为！你们的良心被特朗普吃了吗？抱歉，学术真的有国界。#IEEE下令清理华为系审稿人#", "author": "乌鸦校尉", "time": "乌鸦校尉2019年05月29日22:54", "transmit": 254, "comment": 47, "zan": 327, "sentiment": [{"positive_prob": 0.00137196, "confidence": 0.996951, "negative_prob": 0.998628, "sentiment": 0}]}, {"text": "【北大张海霞教授致信IEEE主席：#北大教授退出IEEE编委#】中国经营报消息，北京大学张海霞教授表示，惊闻IEEE下令禁止华为专家参与期刊审稿，远远超出了一个可以接受学术人的底线，做为IEEE的会员和期刊编委，必须表明自己的态度：“我申请退出我所在的两个IEEE期刊的编委会。”O北大教授张海霞致IEEE主席公开信：申请退出编委会", "author": "新浪科技", "time": "新浪科技2019年05月29日21:05 来自微博weibo.com", "transmit": 79, "comment": 5, "zan": 250, "sentiment": [{"positive_prob": 0.0987694, "confidence": 0.780512, "negative_prob": 0.901231, "sentiment": 0}]}, {"text": "【国际电气学术组织IEEE禁止华为员工参与旗下杂志编审】近日，一则#IEEE下令清理华为系审稿人#的消息登上热搜，邮件中禁止华为员工作为旗下期刊杂志的编辑和审稿人。消息曝出后，不少微博网友直呼“说好的科学无国界呢？”南京大学教授周志华评论称“赤裸裸地干涉学术”。O国际电气学术组织IEEE禁止华为员工参与旗下杂志编审", "author": "新浪科技", "time": "新浪科技2019年05月29日19:30 来自微博weibo.com", "transmit": 40, "comment": 20, "zan": 50, "sentiment": [{"positive_prob": 0.114776, "confidence": 0.744942, "negative_prob": 0.885224, "sentiment": 0}]}, {"text": "【全球最大学术组织#IEEE下令清理华为系审稿人#北大、清华两位教授宣布退出IEEE编委】Sciencewithoutborders?Thistime,maybenot.AninternalemailcirculatingonChinesesocialmediaplatformWeiboshowstheInstituteofElectricalandElectronicsEngineers(IEEE)sayingthattheycannotusecolleaguesfromHuaweiasreviewersoreditorsforthepeer-reviewprocessoftheirjournals.Astheworld'slargesttechnicalprofessionalorganization,IEEEhasmorethan423,000membersinover160countriesandhasareputationforhighlycitedpublications,conferences,technologystandards,andprofessionalandeducationalactivities.Butthistime,theirmovehassparkedhugeoutcryamongacademia.ProfessorZhangHaixia,fromtheInstituteofMicroelectronicsatPekingUniversity,who'salsoaneditorialboardmemberofIEEE,decidedtoquittheeditorialboardsofthetwoIEEEjournalsshewasinvolvedwith.@刘奕群THULiuYiqun,associateprofessorofDepartmentofComputerScienceandTechnologyofTsinghuaUniversityalsoclaimedtowithdrawfromhisacademicpositionatIEEE.#北大教授退出IEEE编委#Formore:O网页链接", "author": "CGTN", "time": "CGTN2019年05月29日21:16 来自微博weibo.com", "transmit": 104, "comment": 55, "zan": 252, "sentiment": [{"positive_prob": 0.98409, "confidence": 0.964645, "negative_prob": 0.0159098, "sentiment": 2}]}, {"text": "【#北大教授回应退出IEEE编委#：禁令超出所有人的底线！】5月29日，IEEE（国际电气与电子工程师协会）发内部邮件下令禁止华为专家参与期刊审稿，北大教授张海霞怒发公开信，请辞IEEE编委一职。她告诉@梨视频文化，这个声明超出科学家、学术人的基本底线。“为什么要去做学术、为什么要参加学术组织？就是因为相信我们是为科学工作，公开、公正、公平是最基本的原则，而这个声明让我们感觉到我们受到严重的挑战。不管是谁，如果他符合去审稿的标准，我都可以邀请他，哪怕这是我的对手，哪怕他是我的敌人，但是绝对不可以，因为他来自哪里，我就我就把他排除在外，这是一个基本的学术原则”。L梨视频文化的秒拍视频", "author": "经济观察报", "time": "经济观察报2019年05月29日21:55 来自微博weibo.com", "transmit": 34, "comment": 17, "zan": 118, "sentiment": [{"positive_prob": 0.0159063, "confidence": 0.964653, "negative_prob": 0.984094, "sentiment": 0}]}, {"text": "一位有态度的知识分子——张海霞。#IEEE下令清理华为系审稿人#", "author": "夜未央_Mars", "time": "夜未央_Mars2019年05月29日17:34 来自iPhone客户端", "transmit": 26, "comment": 8, "zan": 119, "sentiment": [{"positive_prob": 0.644682, "confidence": 0.210405, "negative_prob": 0.355318, "sentiment": 2}]}, {"text": "【#IEEE下令清理华为系审稿人#，全球最大学术组织禁令邮件曝光】5月29日消息，IEEE被曝对华为说不。IEEE（InstituteofElectricalandElectronicsEngineers，国际电气与电子工程师协会），当今世界电子、电气、计算机、通信、自动化工程技术研究领域最著名、规模最大的非营利性跨国学术组织。其最新曝光的相关邮件要求，禁止华为员工作为旗下期刊杂志的编辑和审稿人。接下来，华为相关的投稿、文章、会议赞助，以及分支协会职务，可能都会受到关联影响。邮件中称，由于美国商务部将华为列入黑名单，IEEE做出如下声明：“根据IEEEFAQ文档第12项，我们不能使用华为员工作为期刊的同行评审过程的审稿人或编辑。如果我们继续这样做，可能会产生严重的法律影响。我们可以让华为员工留在编辑委员会，只是不能处理任何文件，直到该公司从商务部黑名单中删除。我们别无选择，只能遵守这些新规定，强烈建议您执行以下程序:如果您的编辑委员会中有华为的编辑，请首先通知他们，并向他们解释情况。一旦您通知了这些编辑，请向所有编辑发送电子邮件，并告知他们不能再使用华为员工作为审稿人。如果目前有论文被分配给华为员工，请建议他们寻找替代者。请编辑向华为审核人员解释情况。”需要说明的是，华为一直是IEEE的重要支持力量。除了员工担任学术职务，作为技术巨头公司，华为与IEEE也有不少合作。这次IEEE发邮件，对华为说No，对于双方之间的合作，也必然会造成影响。对于这件突发的改变，让不少微博网友的三观崩塌。说好的“科学无国界”呢？（量子位）", "author": "中国经营报", "time": "中国经营报2019年05月29日13:57 来自微博weibo.com", "transmit": 142, "comment": 115, "zan": 60, "sentiment": [{"positive_prob": 0.0554989, "confidence": 0.876669, "negative_prob": 0.944501, "sentiment": 0}]}, {"text": "美国今天再次揭下虚伪面具，竟指使国际学术组织IEEE清理华为系审稿人，限制华为专家相关学术活动，把国内某些知识分子常挂在嘴边的“学术无国界”击的粉碎。我们清华、北大两位教授在得知此消息后，第一时间退出IEEE编委会和职务以示抗议，这才是知识分子应有的风骨和态度！#IEEE下令清理华为系审稿人#", "author": "建华Wei业", "time": "建华Wei业2019年05月29日19:34 来自HUAWEIMate20Pro", "transmit": 39, "comment": 37, "zan": 251, "sentiment": [{"positive_prob": 0.213501, "confidence": 0.525554, "negative_prob": 0.786499, "sentiment": 0}]}, {"text": "【全球最大学术组织IEEE下令：清理华为系审稿人！】今日，一封邮件传遍了学术界：国际最大的工业界学术组织IEEEIEEE禁止华为的同事担任审稿人或编辑。接下来，华为相关的投稿、文章、会议赞助，以及分支协会职务，可能都会受到关联影响。邮件显示：可以让华为的同事留在我们的编辑委员会，但他们不能参与任何论文的审阅，直到华为从BIS名单中删除。说好的“科学无国界”呢？", "author": "电脑报", "time": "电脑报2019年05月29日15:21 来自微博weibo.com", "transmit": 224, "comment": 306, "zan": 571, "sentiment": [{"positive_prob": 0.444615, "confidence": 0.0119657, "negative_prob": 0.555385, "sentiment": 0}]}, {"text": 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0.968188, "negative_prob": 0.985685, "sentiment": 0}]}, {"text": "美国🇺🇸现在的不要碧莲是体现在全方位的。还有没有契约精神，说好的学术无国界学术自由呢？IEEE也限制华为专家参与审稿。", "author": "南江何佳", "time": "南江何佳2019年05月29日14:05 来自iPhone8Plus", "transmit": 42, "comment": 62, "zan": 145, "sentiment": [{"positive_prob": 0.0643951, "confidence": 0.8569, "negative_prob": 0.935605, "sentiment": 0}]}, {"text": "IEEE，InstituteofElectricalandElectronicsEngineers，国际电气与电子工程师协会，电子、电气、计算机、通信、自动化工程技术研究领域最著名、规模最大的非营利性跨国学术组织。最新曝光的相关邮件要求，禁止华为员工作为旗下期刊杂志的编辑和审稿人。那些跟我说科学无国界的出来再蹦跶蹦跶呗。。。#IEEE下令清理华为系审稿人#", "author": "贼叉", "time": "贼叉2019年05月29日13:45 来自当世黎曼Android", "transmit": 98, "comment": 108, "zan": 158, "sentiment": [{"positive_prob": 0.917237, "confidence": 0.816082, "negative_prob": 0.0827633, "sentiment": 2}]}] |
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{"Reviews": [{"Title": "Ok for the price", "Author": "DR", "ReviewID": "R2OEN50C6LEWXY", "Overall": "3.0", "Content": "Both chime and alarm modes work and the alarm is very loud. Good alarm for the price. I was hoping to have it mounted in the garage and switch between chime mode when I am home and alarm mode when I am away. There is no way to do this without removing the battery cover. If left in the alarm mode, every time I walk by I have to wait and enter the code. Other than that alarm works as advertised. I have not tried any other alarms and do not know if there is a better one.DR", "Date": "August 25, 2013"}, {"Title": "very good!", "Author": "boxings best", "ReviewID": "RMYE2RO4NYXIC", "Overall": "4.0", "Content": "This is a very good alarm. It's very loud and does what it's suppose to do. The only problem I have is it takes a little too long to alarm once triggered. It take 30 seconds when 20 is enough. otherwise it is a nice alarm for the price.", "Date": "March 29, 2013"}, {"Title": "The Best Motion Alarm I Found on Amazon", "Author": "Amazon Addict", "ReviewID": "R113NL9816YXJD", "Overall": "5.0", "Content": "I looked at probably a dozen motion alarms and settled on this one. I wanted something I could set-up in my office and turn on each evening when I leave. This product is simple, extremely loud, and it works. I mounted it on a high corner of my office. When I enter my office in the morning it lets off a single chirp, which reminds me to enter the password to disarm it. Yes, this could potentially tip off a burglar that you have this alarm, but I would guess most burglars would not even notice it or ignore it. I'm glad it chirps, because otherwise I would probably forget to enter the password and turn it off. I liked that you can great your own password for it also, I set a numeric password that is easy for me to remember. If an intruder hears this thing wailing, they'll probably assume it is a full blown alarm system and that the police are on the way. Inexpensive protection.", "Date": "March 2, 2013"}, {"Title": "It serves its purpose and works!", "Author": "garlandbob", "ReviewID": "RG9YF60LTUBBQ", "Overall": "5.0", "Content": "I bought this unit at a local `brick & mortar' store last summer and I set it every time that I leave my house. It's mounted about 7' off of the floor onto the wall within my front door's entryway, facing down the hallway and also into my living room.Two hours after I left the house this morning, the local police department called my cell phone saying that my house had been broken into. I got home within 3 minutes of that phone call; the police got to my house within 2 minutes of my neighbor calling them. They reported hearing an alarm going off and saw two teenagers running out of my garage that they had pried open because they failed at prying my front door open.Within the 30 second window of the alarm waiting for the pass code to be entered they ransacked two bedrooms. After hearing the alarm squeal, panic must've set in because they left lights on and only made it out of my house with my PS3. My Nikon camera bag with 3 feet away and my home computer was another 2 feet from that.I strongly feel as though this simple alarm did its job. If I didn't have this or some other audible alarm system, I feel that the thieves would've done more damage and would've gotten away with a lot more.Btw: This alarm is still operating on the initial set of batteries that I installed when I got it in July 2012. I think this weekend, Swann cameras are going up around my house.The thieves got away and no good description was given! My neighbor two houses down and across the street heard this alarm. The front door where it's mounted was closed. It was heard at multiple bends; through the living room, into the laundry room, through the garage and out into the neighborhood. It's not ear piercing, but blasting for the duration that it did was loud enough to be greatly effective!Since it's `motion activated', I wish it could be set for instant alarm too once triggered. It's non-adjustable delay is 30 seconds. Other than that, it gets 5 stars!", "Date": "January 29, 2013"}, {"Title": "very good", "Author": "D. Erickson", "ReviewID": "R25SO93VNUJBDB", "Overall": "4.0", "Content": "We have used these type of alarms in several homes over the years. We use them as a deterrent. If someone enters our house or a room, then they'll be surprised by the alarm. It's simple, well made, and practical. Volume is sufficient. No false alarms or problems. We've used this unit about four months now. Battery still strong after this period of time. We had it armed for two weeks during the Christmas break and it held strong during the entire two week period.We don't mount the alarm to the wall. We set it on a table or book shelf. It's a bit wobbly when we set it on a table, so a good design change would be to make it a bit more stable if it's place on a shelf or table top.", "Date": "January 6, 2013"}, {"Title": "Product works fine but is a little too sensative", "Author": "Alfred J. Trimble", "ReviewID": "R2ZOTG1QI3J997", "Overall": "3.0", "Content": "I wanted to use this product to let me know when my puppy approached the back door so that I couldget him out before there was an accident. The angle of coverage was too broad and I not only got the dogbut us as well as we walked around.I think this is a good product for what it was intended and I will take it with me as I travel as a security device.", "Date": "December 31, 2012"}, {"Title": "Very useful", "Author": "Harry", "ReviewID": "R6APZDDZB0KXI", "Overall": "5.0", "Content": "Inexpensive way to guard your property: good quality for the price,the sound that this little thing makes is just UNBEARABLE!!!It's not hard to program it,you can move it from one room to another and I'd recommend it to anyone who doesn't want to spend a fortune for expensive alarm systems. Just make sure to install it on a spot that an intruder won't see,otherwise he'll break it and the unit will stop making noise. And don't forget to replace the batteries every 6 months.", "Date": "September 20, 2012"}], "ProductInfo": {"Price": "$24.99", "Features": "Powerful 120+ Db Siren Sounds When Any Activity Is Detected By Pir Movement Sensor\n120\u00b0 Wide-Angle Detection\n2 Settings - Chime Sound In Alert Mode & Siren In Arm Mode\nCompatible With External Siren, Magnetic Sensor Or Shock Sensor\nEasy To Use With Entry & Exit Delays\nPasscode protected, powerful siren 110dB+ sounds when any activity is detected by PIR movement sensor\n120 degree approximately) wide angle detection with multi-directional mounting bracket\n2 Settings - chime sound (alert mode) & 110dB+ siren (arm mode) selection\nUse in conjunction with other alarm products to maximize your safety", "Name": "Swann Passcode Protected Motion Alarm SW351-KCH", "ImgURL": "http://ecx.images-amazon.com/images/I/316tRg2OqWL._SY300_.jpg", "ProductID": "B003XQITTW"}} |
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{
"actions": [
{
"acted_at": "2003-05-21",
"committee": "Committee on Ways and Means, and in addition to the Committees on Education and the Workforce, and Agriculture, for a period to be subsequently determined by the Speaker, in each case for consideration of such provisions as fall within the jurisdiction of the committee concerned",
"references": [],
"status": "REFERRED",
"text": "Referred to the Committee on Ways and Means, and in addition to the Committees on Education and the Workforce, and Agriculture, for a period to be subsequently determined by the Speaker, in each case for consideration of such provisions as fall within the jurisdiction of the committee concerned.",
"type": "referral"
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{
"acted_at": "2003-05-21",
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"text": "Referred to House Ways and Means",
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},
{
"acted_at": "2003-05-21",
"committee": "House Education and the Workforce",
"references": [],
"text": "Referred to House Education and the Workforce",
"type": "referral"
},
{
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"committee": "House Agriculture",
"references": [],
"text": "Referred to House Agriculture",
"type": "referral"
},
{
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"in_committee": "House Agriculture",
"references": [],
"subcommittee": "Conservation, Credit, Rural Development and Research",
"text": "Referred to the Subcommittee on Conservation, Credit, Rural Development and Research.",
"type": "referral"
},
{
"acted_at": "2003-06-06",
"in_committee": "House Agriculture",
"references": [],
"text": "Executive Comment Requested from USDA.",
"type": "action"
},
{
"acted_at": "2003-06-20",
"in_committee": "House Education and the Workforce",
"references": [],
"subcommittee": "21st Century Competitiveness",
"text": "Referred to the Subcommittee on 21st Century Competitiveness.",
"type": "referral"
}
],
"amendments": [],
"bill_id": "hr2194-108",
"bill_type": "hr",
"committees": [
{
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],
"committee": "House Ways and Means",
"committee_id": "HSWM"
},
{
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],
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"committee_id": "HSED"
},
{
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"subcommittee": "Subcommittee on 21st Century Competitiveness",
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},
{
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],
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"committee_id": "HSAG"
},
{
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],
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"subcommittee": "Subcommittee on Conservation, Credit, Rural Development, and Research",
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}
],
"congress": "108",
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{
"district": null,
"name": "Herseth, Stephanie",
"sponsored_at": "2004-07-21",
"state": "SD",
"thomas_id": "01760",
"title": "Rep",
"withdrawn_at": null
},
{
"district": "3",
"name": "Kind, Ron",
"sponsored_at": "2004-01-27",
"state": "WI",
"thomas_id": "01498",
"title": "Rep",
"withdrawn_at": null
},
{
"district": "3",
"name": "Osborne, Tom",
"sponsored_at": "2003-05-21",
"state": "NE",
"thomas_id": "01658",
"title": "Rep",
"withdrawn_at": null
}
],
"enacted_as": null,
"history": {
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"enacted": false,
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},
"introduced_at": "2003-05-21",
"number": "2194",
"official_title": "To reward the hard work and risk of individuals who choose to live in and help preserve America's small, rural towns, and for other purposes.",
"popular_title": null,
"related_bills": [
{
"bill_id": "hr1686-108",
"reason": "related"
},
{
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"reason": "identical"
}
],
"short_title": "New Homestead Act of 2003",
"sponsor": {
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"name": "Pomeroy, Earl",
"state": "ND",
"thomas_id": "00922",
"title": "Rep",
"type": "person"
},
"status": "REFERRED",
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"subjects": [
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"Agricultural credit",
"Agriculture and food",
"Bank accounts",
"Capital gains tax",
"Capital investments",
"Catastrophic health insurance",
"College costs",
"Commerce",
"Construction costs",
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"Cost of living adjustments",
"County politics and government",
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"Earned income tax credit",
"Economics and public finance",
"Education",
"Electrification",
"Energy",
"Entrepreneurs",
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"Federal aid to education",
"Federally-guaranteed loans",
"Finance and financial sector",
"Government operations and politics",
"Government paperwork",
"Health",
"Higher education",
"Home ownership",
"Housing and community development",
"Housing finance",
"Income tax",
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"Infrastructure",
"Infrastructure (Economics)",
"Internal migration",
"Investment tax credit",
"Job creation",
"Labor and employment",
"Law",
"Medical economics",
"Minimum tax",
"Nonprofit organizations",
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"Rural economic development",
"Rural education",
"Rural housing",
"Saving and investment",
"Savings and loan associations",
"Scholarships",
"Securities",
"Small business",
"Small towns",
"Social welfare",
"Student loan funds",
"Tax credits",
"Tax deductions",
"Tax exclusion",
"Tax exemption",
"Tax penalties",
"Tax returns",
"Tax-exempt organizations",
"Trusts and trustees",
"Urban affairs",
"Venture capital",
"Vocational education"
],
"subjects_top_term": "Housing and community development",
"summary": {
"as": "Introduced",
"date": "2003-05-21",
"text": "New Homestead Act of 2003 - Directs the Secretary of Education to repay a specified percentage (up to $2,000 per year) of certain education loans to an individual who completes a degree and both resides and is employed in a qualifying county outside a metropolitan statistical area which for 20 years has a net out-migration of inhabitants of at least ten percent of the county population.Amends the Internal Revenue Code to allow a tax credit for a specified portion of the purchase price of a qualified residence in such a rural county.Allows a capital loss deduction with respect to sale or exchange of principal residence in certain rural areas.Provides for the creation of tax-exempt individual homestead accounts, which shall receive matching Federal contributions according to a specified formula, whose tax-free distributions after five years are used for qualified higher education or medical expenses, first-time homebuyer or business capitalization costs, or rollovers.Establishes a rural investment tax credit of 70 percent of the present value of new buildings (including rehabilitation projects) or 30 percent of the present value of existing buildings.Establishes a qualified rural investment small business investment credit of 30 percent of qualified expenditures.Provides for accelerated depreciation of property in rural investment projects.New Homestead Venture Capital Fund Act - Amends the Consolidated Farm and Rural Development Act to provide for establishment of new homestead venture capital funds, owned by private investors, which shall generate and provide equity capital to rural businesses."
},
"titles": [
{
"as": "introduced",
"title": "New Homestead Act of 2003",
"type": "short"
},
{
"as": "introduced",
"title": "New Homestead Venture Capital Fund Act",
"type": "short"
},
{
"as": "introduced",
"title": "To reward the hard work and risk of individuals who choose to live in and help preserve America's small, rural towns, and for other purposes.",
"type": "official"
}
],
"updated_at": "2013-02-02T20:34:34-05:00"
} |
{".js":"aa66af44461a5e46feaebf04ff77fabde99ad0fa.js"} |
{"Self-Adaptive Applications on the Grid\nGosia Wrzesinska Jason Maassen Henri E. Bal\nDept. of Computer Systems, Vrije Universiteit Amsterdam\n{gosia, jason, bal}@cs.vu.nl\nAbstract\nGrids are inherently heterogeneous and dynamic. One important\nproblem in grid computing is resource selection, that is, finding\nan appropriate resource set for the application. Another problem\nis adaptation to the changing characteristics of the grid \nenvironment. Existing solutions to these two problems require that a \nperformance model for an application is known. However, \nconstructing such models is a complex task. In this paper, we investigate\nan approach that does not require performance models. We start an\napplication on any set of resources. During the application run, we\nperiodically collect the statistics about the application run and \ndeduce application requirements from these statistics. Then, we adjust\nthe resource set to better fit the application needs. This approach \nallows us to avoid performance bottlenecks, such as overloaded WAN\nlinks or very slow processors, and therefore can yield significant\nperformance improvements. We evaluate our approach in a number\nof scenarios typical for the Grid.\nCategories and Subject Descriptors C.2.4 [COMPUTER \nCOMMUNICATION NETWORKS]: Distributed Systems-Distributed\napplications\n; C.4 [PERFORMANCE OF SYSTEMS]: Measurement \ntechniques, Modelling techniques\nGeneral Terms Algorithms, Measurement, Performance, \nExperimentation\n1. Introduction\nIn recent years, grid computing has become a real alternative to \ntraditional parallel computing. A grid provides much computational\npower, and thus offers the possibility to solve very large problems,\nespecially if applications can run on multiple sites at the same time\n(7; 15; 20). However, the complexity of Grid environments also\nis many times larger than that of traditional parallel machines like\nclusters and supercomputers. One important problem is resource\nselection - selecting a set of compute nodes such that the \napplication achieves good performance. Even in traditional, \nhomogeneous parallel environments, finding the optimal number of nodes\nis a hard problem and is often solved in a trial-and-error fashion.\nIn a grid environment this problem is even more difficult, because\nof the heterogeneity of resources: the compute nodes have various\nspeeds and the quality of network connections between them varies\nfrom low-latency and high-bandwidth local-area networks (LANs)\nto high-latency and possibly low-bandwidth wide-area networks\n(WANs). Another important problem is that the performance and\navailability of grid resources varies over time: the network links or\ncompute nodes may become overloaded, or the compute nodes may\nbecome unavailable because of crashes or because they have been\nclaimed by a higher priority application. Also, new, better resources\nmay become available. To maintain a reasonable performance level,\nthe application therefore needs to adapt to the changing conditions.\nThe adaptation problem can be reduced to the resource selection\nproblem: the resource selection phase can be repeated during \napplication execution, either at regular intervals, or when a performance\nproblem is detected, or when new resources become available. This\napproach has been adopted by a number of systems (5; 14; 18). For\nresource selection, the application runtime is estimated for some\nresource sets and the set that yields the shortest runtime is selected\nfor execution. Predicting the application runtime on a given set of\nresources, however, requires knowledge about the application. \nTypically, an analytical performance model is used, but constructing\nsuch a model is inherently difficult and requires an expertise which\napplication programmers may not have.\nIn this paper, we introduce and evaluate an alternative approach\nto application adaptation and resource selection which does not\nneed a performance model. We start an application on any set of\nresources. During the application run, we periodically collect \ninformation about the communication times and idle times of the \nprocessors. We use these statistics to automatically estimate the resource\nrequirements of the application. Next, we adjust the resource set the\napplication is running on by adding or removing compute nodes\nor even entire clusters. Our adaptation strategy uses the work by\nEager et al. (10) to determine the efficiency and tries to keep the\nefficiency of the application between a lower and upper threshold\nderived from their theory. Processors are added or deleted to stay\nbetween the thresholds, thus adapting automatically to the \nchanging environment.\nA major advantage of our approach is that it improves \napplication performance in many different situations that are typical for\ngrid computing. It handles all of the following cases:\n\u00e2\u20ac\u00a2 automatically adapting the number of processors to the degree\nof parallelism in the application, even when this degree changes\ndynamically during the computation\n\u00e2\u20ac\u00a2 migrating (part of) a computation away from overloaded \nresources\n\u00e2\u20ac\u00a2 removing resources with poor communication links that slow\ndown the computation\n\u00e2\u20ac\u00a2 adding new resources to replace resources that have crashed\nOur work assumes the application is malleable and can run \n(efficiently) on multiple sites of a grid (i.e., using co-allocation (15)).\nIt should not use static load balancing or be very sensitive to \nwide121\narea latencies. We have applied our ideas to divide-and-conquer\napplications, which satisfy these requirements. Divide-and-conquer\nhas been shown to be an attractive paradigm for programming grid\napplications (4; 20). We believe that our approach can be extended\nto other classes of applications with the given assumptions.\nWe implemented our strategy in Satin, which is a Java-centric\nframework for writing grid-enabled divide-and-conquer \napplications (20). We evaluate the performance of our approach on the\nDAS-2 wide-area system and we will show that our approach yields\nmajor performance improvements (roughly 10-60 %) in the above\nscenarios.\nThe rest of this paper is structured as follows. In Section 2, we\nexplain what assumptions we are making about the applications and\ngrid resources. In Section 3, we present our resource selection and\nadaptation strategy. In Section 4, we describe its implementation in\nthe Satin framework. In Section 5, we evaluate our approach in a\nnumber of grid scenarios. In Section 6, we compare our approach\nwith the related work. Finally, in Section 7, we conclude and \ndescribe future work.\n2. Background and assumptions\nIn this section, we describe our assumptions about the applications\nand their resources. We assume the following resource model. The\napplications are running on multiple sites at the same time, where\nsites are clusters or supercomputers. We also assume that the \nprocessors of the sites are accessible using a grid scheduling system,\nsuch as Koala (15), Zorilla (9) or GRMS (3). Processors \nbelonging to one site are connected by a fast LAN with a low latency\nand high bandwidth. The different sites are connected by a WAN.\nCommunication between sites suffers from high latencies. We \nassume that the links connecting the sites with the Internet backbone\nmight become bottlenecks causing the inter-site communication to\nsuffer from low bandwidths.\nWe studied the adaptation problem in the context of \ndivide-andconquer applications. However, we believe that our methodology\ncan be used for other types of applications as well. In this section\nwe summarize the assumptions about applications that are \nimportant to our approach.\nThe first assumption we make is that the application is \nmalleable, i.e., it is able to handle processors joining and leaving\nthe on-going computation. In (23), we showed how \ndivide-andconquer applications can be made fault tolerant and malleable.\nProcessors can be added or removed at any point in the \ncomputation with little overhead. The second assumption is that the \napplication can efficiently run on processors with different speeds.\nThis can be achieved by using a dynamic load balancing \nstrategy, such as work stealing used by divide-and-conquer \napplications (19). Also, master-worker applications typically use dynamic\nload-balancing strategies (e.g., MW - a framework for writing \ngridenabled master-worker applications (12)). We find it a reasonable\nassumption for a grid application, since applications for which the\nslowest processor becomes a bottleneck will not be able to \nefficiently utilize grid resources. Finally, the application should be \ninsensitive to wide-area latencies, so it can run efficiently on a \nwidearea grid (16; 17).\n3. Self-adaptation\nIn this section we will explain how we use application \nmalleability to find a suitable set of resources for a given application and to\nadapt to changing conditions in the grid environment. In order to\nmonitor the application performance and guide the adaptation, we\nadded an extra process to the computation which we call \nadaptation coordinator. The adaptation coordinator periodically collects\nperformance statistics from the application processors. We \nintroduce a new application performance metric: weighted average \nefficiency which describes the application performance on a \nheterogeneous set of resources. The coordinator uses statistics from \napplication processors to compute the weighted average efficiency. If\nthe efficiency falls above or below certain thresholds, the \ncoordinator decides on adding or removing processors. A heuristic formula\nis used to decide which processors have to be removed. During\nthis process the coordinator learns the application requirements by\nremembering the characteristics of the removed processors. These\nrequirements are then used to guide the adding of new processors.\n3.1 Weighted average efficiency\nIn traditional parallel computing, a standard metric describing the\nperformance of a parallel application is efficiency. Efficiency is\ndefined as the average utilization of the processors, that is, the\nfraction of time the processors spend doing useful work rather than\nbeing idle or communicating with other processors (10).\nefficiency =\n1\nn\n\u00e2\u02c6\u2014\nn\ni=0\n(1 \u00e2\u02c6\u2019 overheadi )\nwhere n is the number of processors and overheadi is the \nfraction of time the ith\nprocessor spends being idle or communicating.\nEfficiency indicates the benefit of using multiple processors.\nTypically, the efficiency drops as new processors are added to\nthe computation. Therefore, achieving a high speedup (and thus\na low execution time) and achieving a high system utilization are\nconflicting goals (10). The optimal number of processors is the\nnumber for which the ratio of efficiency to execution time is \nmaximized. Adding processors beyond this number yields little benefit.\nThis number is typically hard to find, but in (10) it was \ntheoretically proven that if the optimal number of processors is used, the\nefficiency is at least 50%. Therefore, adding processors when \nefficiency is smaller or equal to 50% will only decrease the system\nutilization without significant performance gains.\nFor heterogeneous environments with different processor speeds,\nwe extended the notion of efficiency and introduced weighted \naverage efficiency.\nwa efficiency =\n1\nn\n\u00e2\u02c6\u2014\nn\ni=0\nspeedi \u00e2\u02c6\u2014 (1 \u00e2\u02c6\u2019 overheadi )\nThe useful work done by a processor (1 \u00e2\u02c6\u2019 overheadi) is\nweighted by multiplying it by the speed of this processor \nrelative to the fastest processor. The fastest processor has speed = 1,\nfor others holds: 0 < speed \u00e2\u2030\u00a4 1. Therefore, slower processors are\nmodeled as fast ones that spend a large fraction of the time being\nidle. Weighted average efficiency reflects the fact that adding slow\nprocessors yields less benefit than adding fast processors.\nIn the heterogeneous world, it is hardly beneficial to add \nprocessors if the efficiency is lower than 50% unless the added processor\nis faster than some of the currently used processors. Adding faster\nprocessors might be beneficial regardless of the efficiency.\n3.2 Application monitoring\nEach processor measures the time it spends communicating or \nbeing idle. The computation is divided into monitoring periods. \nAfter each monitoring period, the processors compute their overhead\nover this period as the percentage of the time they spent being idle\nor communicating in this period. Apart from total overhead, each\nprocessor also computes the overhead of inter-cluster and \nintracluster communication.\nTo calculate weighted average efficiency, we need to know the\nrelative speeds of the processors, which depend on the \napplication and the problem size used. Since it is impractical to run the\n122\nwhole application on each processor separately, we use \napplicationspecific benchmarks. Currently we use the same application with a\nsmall problem size as a benchmark and we require the application\nprogrammer to specify this problem size. This approach requires\nextra effort from the programmer to find the right problem size and\npossibly to produce input files for this problem size, which may\nbe hard. In the future, we are planning to generate benchmarks \nautomatically by choosing a random subset of the task graph of the\noriginal application.\nBenchmarks have to be re-run periodically because the speed of\na processor might change if it becomes overloaded by another \napplication (for time-shared machines). There is a trade-off between\nthe accuracy of speed measurements and the overhead it incurs. The\nlonger the benchmark, the greater the accuracy of the measurement.\nThe more often it is run, the faster changes in processor speed are\ndetected. In our current implementation, the application \nprogrammer specifies the length of the benchmark (by specifying its \nproblem size) and the maximal overhead it is allowed to cause. \nProcessors run the benchmark at such frequency so as not to exceed the\nspecified overhead. In the future, we plan to combine \nbenchmarking with monitoring the load of the processor which would allow\nus to avoid running the benchmark if no change in processor load\nis detected. This optimization will further reduce the benchmarking\noverhead.\nNote that the benchmarking overhead could be avoided \ncompletely for more regular applications, for example, for \nmasterworker applications with tasks of equal or similar size. The \nprocessor speed could then be measured by counting the tasks \nprocessed by this processor within one monitoring period. \nUnfortunately, divide-and-conquer applications typically exhibit a very \nirregular structure. The sizes of tasks can vary by many orders of\nmagnitude.\nAt the end of each monitoring period, the processors send the\noverhead statistics and processor speeds to the coordinator. \nPeriodically, the coordinator computes the weighted average efficiency and\nother statistics, such as average inter-cluster overhead or overheads\nin each cluster. The clocks of the processors are not synchronized\nwith each other or with the clock of the coordinator. Each \nprocessor decides separately when it is time to send data. Occasionally,\nthe coordinator may miss data at the end of a monitoring period,\nso it has to use data from the previous monitoring period for these\nprocessors. This causes small inaccuracies in the calculations of the\ncoordinator, but does not influence the performance of adaptation.\n3.3 Adaptation strategy\nThe adaptation coordinator tries to keep the weighted average \nefficiency between Emin and Emax. When it exceeds Emax, the \ncoordinator requests new processors from the scheduler. The number of\nrequested processors depends on the current efficiency: the higher\nthe efficiency, the more processors are requested. The coordinator\nstarts removing processors when the weighted average efficiency\ndrops below Emin. The number of nodes that are removed again\ndepends on the weighted average efficiency. The lower the \nefficiency, the more nodes are removed. The thresholds we use are\nEmax = 50%, because we know that adding processors when \nefficiency is lower does not make sense, and Emin = 30%. \nEfficiency of 30% or lower might indicate performance problems such\nas low bandwidth or overloaded processors. In that case, \nremoving bad processors will be beneficial for the application. Such low\nefficiency might also indicate that we simply have too many \nprocessors. In that case, removing some processors may not be beneficial\nbut it will not harm the application. The coordinator always tries\nto remove the worst processors. The badness of a processor is\ndetermined by the following formula:\nproc badnessi = \u00ce\u00b1 \u00e2\u02c6\u2014\n1\nspeedi\n+ \u00ce\u00b2 \u00e2\u02c6\u2014 ic overheadi\n+ \u00ce\u00b3 \u00e2\u02c6\u2014 inW orstCluster(i)\nThe processor is considered bad if it has low speed ( 1\nspeed\nis\nbig) and high inter-cluster overhead (ic overhead). High \nintercluster overhead indicates that the bandwidth to this processor\"s\ncluster is insufficient. Removing processors located in a single\ncluster is desirable since it decreases the amount of wide-area\ncommunication. Therefore, processors belonging to the worst\ncluster are preferred. Function inW orstCluster(i) returns 1 for\nprocessors belonging to the worst cluster and 0 otherwise. The\nbadness of clusters is computed similarly to the badness of\nprocessors:\ncluster badnessi = \u00ce\u00b1 \u00e2\u02c6\u2014\n1\nspeedi\n+ \u00ce\u00b2 \u00e2\u02c6\u2014 ic overheadi\nThe speed of a cluster is the sum of processor speeds normalized\nto the speed of the fastest cluster. The ic overhead of a cluster is\nan average of processor inter-cluster overheads. The \u00ce\u00b1, \u00ce\u00b2 and \u00ce\u00b3\ncoefficients determine the relative importance of the terms. Those\ncoefficients are established empirically. Currently we are using\nthe following values: \u00ce\u00b1 = 1, \u00ce\u00b2 = 100 and \u00ce\u00b3 = 10, based\non the observation that ic overhead > 0.2 indicates bandwidth\nproblems and processors with speed < 0.05 do not contribute to\nthe computation.\nAdditionally, when one of the clusters has an exceptionally high\ninter-cluster overhead (larger than 0.25), we conclude that the \nbandwidth on the link between this cluster and the Internet backbone is\ninsufficient for the application. In that case, we simply remove the\nwhole cluster instead of computing node badness and removing the\nworst nodes. After deciding which nodes are removed, the \ncoordinator sends a message to these nodes and the nodes leave the\ncomputation. Figure 1 shows a schematic view of the adaptation\nstrategy. Dashed lines indicate a part that is not supported yet, as\nwill be explained below.\nThis simple adaptation strategy allows us to improve application\nperformance in several situations typical for the Grid:\n\u00e2\u20ac\u00a2 If an application is started on fewer processors than its degree of\nparallelism allows, it will automatically expand to more \nprocessors (as soon as there are extra resources available). Conversely,\nif an application is started on more processors than it can \nefficiently use, a part of the processors will be released.\n\u00e2\u20ac\u00a2 If an application is running on an appropriate set of resources\nbut after a while some of the resources (processors and/or \nnetwork links) become overloaded and slow down the \ncomputation, the overloaded resources will be removed. After removing\nthe overloaded resources, the weighted average efficiency will\nincrease to above the Emax threshold and the adaptation \ncoordinator will try to add new resources. Therefore, the application\nwill be migrated from overloaded resources.\n\u00e2\u20ac\u00a2 If some of the original resources chosen by the user are \ninappropriate for the application, for example the bandwidth to one\nof the clusters is too small, the inappropriate resources will be\nremoved. If necessary, the adaptation component will try to add\nother resources.\n\u00e2\u20ac\u00a2 If during the computation a substantial part of the processors\nwill crash, the adaptation component will try to add new \nresources to replace the crashed processors.\n123\n0\n2000\n4000\n6000\nruntime(secs.)\nScenario 0\na b c\nScenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5\nwithout monitoring and adaptation (runtime 1)\nwith monitoring and adaptation (runtime 2)\nwith monitoring but no adaptation (runtime 3)\nFigure 2. The runtimes of the Barnes-Hut application, scenarios 0-5\nadd nodes\nfaster nodes\navailable\nif\ncompute weighted\naverage efficiency E wa\nwait & collect\nstatistics\nrank nodes\nremove worst nodes\nwaE\nEwa\nY\nN\nN\nY\nabove\nif\nbelow\nif\nEmin\nmaxE\nFigure 1. Adaptation strategy\n\u00e2\u20ac\u00a2 If the application degree of parallelism is changing during the\ncomputation, the number of nodes the application is running on\nwill be automatically adjusted.\nFurther improvements are possible, but require extra \nfunctionality from the grid scheduler and/or integration with monitoring\nservices such as NWS (22). For example, adding nodes to a \ncomputation can be improved. Currently, we add any nodes the \nscheduler gives us. However, it would be more efficient to ask for the\nfastest processors among the available ones. This could be done, for\nexample, by passing a benchmark to the grid scheduler, so that it\ncan measure processor speeds in an application specific way. \nTypically, it would be enough to measure the speed of one processor\nper site, since clusters and supercomputers are usually \nhomogeneous. An alternative approach would be ranking the processors\nbased on parameters such as clock speed and cache size. This \napproach is sometimes used for resource selection for sequential \napplications (14). However, it is less accurate than using an \napplication specific benchmark.\nAlso, during application execution, we can learn some \napplication requirements and pass them to the scheduler. One example is\nminimal bandwidth required by the application. The lower bound\non minimal required bandwidth is tightened each time a cluster\nwith high inter-cluster overhead is removed. The bandwidth \nbetween each pair of clusters is estimated during the computation by\nmeasuring data transfer times, and the bandwidth to the removed\ncluster is set as a minimum. Alternatively, information from a grid\nmonitoring system can be used. Such bounds can be passed to the\nscheduler to avoid adding inappropriate resources. It is especially\nimportant when migrating from resources that cause performance\nproblems: we have to be careful not to add the resources we have\njust removed. Currently we use blacklisting - we simply do not \nallow adding resources we removed before. This means, however,\nthat we cannot use these resources even if the cause of the \nperformance problem disappears, e.g. the bandwidth of a link might\nimprove if the background traffic diminishes.\nWe are currently not able to perform opportunistic migration\n- migrating to better resources when they are discovered. If an\napplication runs with efficiency between Emin and Emax, the\nadaptation component will not undertake any action, even if \nbetter resources become available. Enabling opportunistic migration\nrequires, again, the ability to specify to the scheduler what \nbetter resources are (faster, with a certain minimal bandwidth) and\nreceiving notifications when such resources become available.\nExisting grid schedulers such as GRAM from the Globus\nToolkit (11) do not support such functionality. The developers of\nthe KOALA metascheduler (15) have recently started a project\nwhose goal is to provide support for adaptive applications. We\nare currently discussing with them the possibility of providing the\nfunctionalities required by us, aiming to extend our adaptivity \nstrat124\negy to support opportunistic migration and to improve the initial\nresource selection.\n4. Implementation\nWe incorporated our adaptation mechanism into Satin - a Java\nframework for creating grid-enabled divide-and-conquer \napplications. With Satin, the programmer annotates the sequential code\nwith divide-and-conquer primitives and compiles the annotated\ncode with a special Satin compiler that generates the necessary\ncommunication and load balancing code. Satin uses a very \nefficient, grid-aware load balancing algorithm - Cluster-aware \nRandom Work Stealing (CRS) (19), which hides wide-area latencies\nby overlapping local and remote stealing. Satin also provides \ntransparent fault tolerance and malleability (23). With Satin, removing\nand adding processors from/to an ongoing computation incurs little\noverhead.\nWe instrumented the Satin runtime system to collect runtime\nstatistics and send them to the adaptation coordinator. The \ncoordinator is implemented as a separate process. Both coordinator and\nSatin are implemented entirely in Java on top of the Ibis \ncommunication library (21). The core of Ibis is also implemented in Java.\nThe resulting system therefore is highly portable (due to Java\"s\nwrite once, run anywhere property) allowing the software to run\nunmodified on a heterogeneous grid.\nIbis also provides the Ibis Registry. The Registry provides,\namong others, a membership service to the processors taking part\nin the computation. The adaptation coordinator uses the Registry\nto discover the application processes, and the application processes\nuse this service to discover each other. The Registry also offers fault\ndetection (additional to the fault detection provided by the \ncommunication channels). Finally, the Registry provides the possibility\nto send signals to application processes. The coordinator uses this\nfunctionality to notify the processors that they need to leave the\ncomputation. Currently the Registry is implemented as a \ncentralized server.\nFor requesting new nodes, the Zorilla (9) system is used - a\npeer-to-peer supercomputing middleware which allows \nstraightforward allocation of processors in multiple clusters and/or \nsupercomputers. Zorilla provides locality-aware scheduling, which tries to\nallocate processors that are located close to each other in terms\nof communication latency. In the future, Zorilla will also support\nbandwidth-aware scheduling, which tries to maximize the total\nbandwidth in the system. Zorilla can be easily replaced with \nanother grid scheduler. In the future, we are planning to integrate\nour adaptation component with GAT (3) which is becoming a \nstandard in the grid community and KOALA (15) a scheduler that \nprovides co-allocation on top of standard grid middleware, such as the\nGlobus Toolkit (11).\n5. Performance evaluation\nIn this section, we will evaluate our approach. We will demonstrate\nthe performance of our mechanism in a few scenarios. The first\nscenario is an ideal situation: the application runs on a \nreasonable set of nodes (i.e., such that the efficiency is around 50%) and\nno problems such as overloaded networks and processors, \ncrashing processors etc. occur. This scenario allows us to measure the\noverhead of the adaptation support. The remaining scenarios are\ntypical for grid environments and demonstrate that with our \nadaptation support the application can avoid serious performance \nbottlenecks such as overloaded processors or network links. For each \nscenario, we compare the performance of an application with \nadaptation support to a non-adaptive version. In the non-adaptive version,\nthe coordinator does not collect statistics and no benchmarking (for\nmeasuring processor speeds) is performed. In the ideal scenario,\n0 5 10 15\niteration number\n0\n200\n400\n600\niterationduration(secs.)\nstarting on 8 nodes\nstarting on 16 nodes\nstarting on 24 nodes\nstarting on 8 nodes\nstarting on 16 nodes\nstarting on 24 nodes\n}no adaptation\n}with adaptation\nFigure 3. Barnes-Hut iteration durations with/without adaptation,\ntoo few processors\n0 5 10 15\niteration number\n0\n200\n400\n600\n800\n1000\niterationduration(secs.)\nno adaptation\nwith adaptation\nCPU load introduced\noverloaded nodes removed\nstarted adding nodes\n36 nodes reached\nFigure 4. Barnes-Hut iteration durations with/without adaptation,\noverloaded CPUs\nwe additionally measure the performance of an application with\ncollecting statistics and benchmarking turned on but without \ndoing adaptation, that is, without allowing it to change the number\nof nodes. This allows us to measure the overhead of benchmarking\nand collecting statistics. In all experiments we used a monitoring\nperiod of 3 minutes for the adaptive versions of the applications.\nAll the experiments were carried out on the DAS-2 wide-area \nsystem (8), which consists of five clusters located at five Dutch \nuni125\nversities. One of the clusters consists of 72 nodes, the others of 32\nnodes. Each node contains two 1 GHz Pentium processors. Within\na cluster, the nodes are connected by Fast Ethernet. The clusters\nare connected by the Dutch university Internet backbone. In our\nexperiments, we used the Barnes-Hut N-body simulation. \nBarnesHut simulates the evolution of a large set of bodies under influence\nof (gravitational or electrostatic) forces. The evolution of N bodies\nis simulated in iterations of discrete time steps.\n5.1 Scenario 0: adaptivity overhead\nIn this scenario, the application is started on 36 nodes. The nodes\nare equally divided over 3 clusters (12 nodes in each cluster). On\nthis number of nodes, the application runs with 50% efficiency, so\nwe consider it a reasonable number of nodes. As mentioned above,\nin this scenario we measured three runtimes: the runtime of the \napplication without adaptation support (runtime 1), the runtime with\nadaptation support (runtime 2) and the runtime with monitoring\n(i.e., collection of statistics and benchmarking) turned on but \nwithout allowing it to change the number of nodes (runtime 3). Those\nruntimes are shown in Figure 2, first group of bars. The comparison\nbetween runtime 3 and 1 shows the overhead of adaptation support.\nIn this experiment it is around 15%. Almost all overhead comes\nfrom benchmarking. The benchmark is run 1-2 times per \nmonitoring period. This overhead can be made smaller by increasing the\nlength of the monitoring period and decreasing the benchmarking\nfrequency. The monitoring period we used (3 minutes) is relatively\nshort, because the runtime of the application was also relatively\nshort (30-60 minutes). Using longer running applications would\nnot allow us to finish the experimentation in a reasonable time.\nHowever, real-world grid applications typically need hours, days or\neven weeks to complete. For such applications, a much longer \nmonitoring period can be used and the adaptation overhead can be kept\nmuch lower. For example, with the Barnes-Hut application, if the\nmonitoring period is extended to 10 minutes, the overhead drops to\n6%. Note that combining benchmarking with monitoring processor\nload (as described in Section 3.2) would reduce the benchmarking\noverhead to almost zero: since the processor load is not changing,\nthe benchmarks would only need to be run at the beginning of the\ncomputation.\n5.2 Scenario 1: expanding to more nodes\nIn this scenario, the application is started on fewer nodes than the\napplication can efficiently use. This may happen because the user\ndoes not know the right number of nodes or because insufficient\nnodes were available at the moment the application was started. We\ntried 3 initial numbers of nodes: 8 (Scenario 1a), 16 (Scenario 1b)\nand 24 (Scenario 1c). The nodes were located in 1 or 2 clusters. In\neach of the three sub-scenarios, the application gradually expanded\nto 36-40 nodes located in 4 clusters. This allowed to reduce the\napplication runtimes by 50% (Scenario 1a), 35% (Scenario 1b) and\n12% (Scenario 1c) with respect to the non-adaptive version. Those\nruntimes are shown in Figure 2. Since Barnes-Hut is an iterative\napplication, we also measured the time of each iteration, as shown\nin Figure 3. Adaptation reduces the iteration time by a factor of 3\n(Scenario 1a), 1.7 (Scenario 1b) and 1.2 (Scenario 1c) which allows\nus to conclude that the gains in the total runtime would be even\nbigger if the application were run longer than for 15 iterations.\n5.3 Scenario 2: overloaded processors\nIn this scenario, we started the application on 36 nodes in 3 clusters.\nAfter 200 seconds, we introduced a heavy, artificial load on the \nprocessors in one of the clusters. Such a situation may happen when\nan application with a higher priority is started on some of the \nresources. Figure 4 shows the iteration durations of both the adaptive\nand non-adaptive versions. After introducing the load, the iteration\n0 5 10 15\niteration number\n0\n200\n400\n600\n800\n1000\niterationduration(secs.)\nno adaptation\nwith adaptation\none cluster is badly connected\nbadly connected cluster removed\nstarted adding nodes\n36 nodes reached\nFigure 5. Barnes-Hut iteration durations with/without adaptation,\noverloaded network link\n0 5 10 15\niteration number\n0\n200\n400\n600\n800\n1000\niterationduration(secs.)\nno adaptation\nwith adaptation\none cluster is badly connected\n12 nodes lightly overloaded\nremoved badly connected cluster\nremoved 2 lightly overloaded nodes\nFigure 6. Barnes-Hut iteration durations with/without adaptation,\noverloaded CPUs and an overloaded network link\nduration increased by a factor of 2 to 3. Also, the iteration times\nbecame very variable. The adaptive version reacted by removing\nthe overloaded nodes. After removing these nodes, the weighted\naverage efficiency rose to around 35% which triggered adding new\nnodes and the application expanded back to 38 nodes. So, the \noverloaded nodes were replaced by better nodes, which brought the \niteration duration back to the initial values. This reduced the total\nruntime by 14%. The runtimes are shown in Figure 2.\n126\n5.4 Scenario 3: overloaded network link\nIn this scenario, we ran the application on 36 nodes in 3 clusters.\nWe simulated that the uplink to one of the clusters was overloaded\nand the bandwidth on this uplink was reduced to approximately\n100 KB/s. To simulate low bandwidth we use the traffic-shaping\ntechniques described in (6). The iteration durations in this \nexperiment are shown in Figure 5. The iteration durations of the \nnonadaptive version exhibit enormous variation: from 170 to 890 \nseconds. The adaptive version removed the badly connected cluster\nafter the first monitoring period. As a result, the weighted \naverage efficiency rose to around 35% and new nodes were gradually\nadded until their number reached 38. This brought the iteration\ntimes down to around 100 seconds. The total runtime was reduced\nby 60% (Figure 2).\n5.5 Scenario 4: overloaded processors and an overloaded\nnetwork link\nIn this scenario, we ran the application on 36 nodes in 3 clusters.\nAgain, we simulated an overloaded uplink to one of the clusters.\nAdditionally, we simulated processors with heterogeneous speeds\nby inserting a relatively light artificial load on the processors in one\nof the remaining clusters. The iteration durations are shown in \nFigure 6. Again, the non-adaptive version exhibits a great variation in\niteration durations: from 200 to 1150 seconds. The adaptive \nversion removes the badly connected cluster after the first monitoring\nperiod which brings the iteration duration down to 210 seconds on\naverage. After removing one of the clusters, since some of the \nprocessors are slower (approximately 5 times), the weighted average\nefficiency raises only to around 40%. Since this value lies between\nEmin and Emax, no nodes are added or removed. This example \nillustrates what the advantages of opportunistic migration would be.\nThere were faster nodes available in the system. If these nodes were\nadded to the application (which could trigger removing the slower\nnodes) the iteration duration could be reduced even further. Still,\nthe adaptation reduced the total runtime by 30% (Figure 2).\n5.6 Scenario 5: crashing nodes\nIn the last scenario, we also run the application on 36 nodes in 3\nclusters. After 500 seconds, 2 out of 3 clusters crash. The iteration\ndurations are shown in Figure 7. After the crash, the iteration\nduration raised from 100 to 200 seconds. The weighted efficiency\nrose to around 30% which triggered adding new nodes in the\nadaptive version. The number of nodes gradually went back to 35\nwhich brought the iteration duration back to around 100 seconds.\nThe total runtime was reduced by 13% (Figure 2).\n6. Related work\nA number of Grid projects address the question of resource \nselection and adaptation. In GrADS (18) and ASSIST (1), resource \nselection and adaptation requires a performance model that allows\npredicting application runtimes. In the resource selection phase, a\nnumber of possible resource sets is examined and the set of \nresources with the shortest predicted runtime is selected. If \nperformance degradation is detected during the computation, the resource\nselection phase is repeated. GrADS uses the ratio of the predicted\nexecution times (of certain application phases) to the real \nexecution times as an indicator of application performance. ASSIST uses\nthe number of iterations per time unit (for iterative applications)\nor the number of tasks per time unit (for regular master-worker\napplications) as a performance indicator. The main difference \nbetween these approaches and our approach is the use of performance\nmodels. The main advantage is that once the performance model\nis known, the system is able to take more accurate migration \ndecisions than with our approach. However, even if the performance\n0 5 10 15\niteration number\n0\n200\n400\n600\n800\n1000\niterationduration(secs.)\nno adaptation\nwith adaptation\n2 out of 3 clusters crash\nstarted adding nodes\n36 nodes reached\nFigure 7. Barnes-Hut iteration durations with/without adaptation,\ncrashing CPUs\nmodel is known, the problem of finding an optimal resource set (i.e.\nthe resource set with the minimal execution time) is NP-complete.\nCurrently, both GrADS and ASSIST examine only a subset of all\npossible resource sets and therefore there is no guarantee that the\nresulting resource set will be optimal. As the number of available\ngrid resources increases, the accuracy of this approach diminishes,\nas the subset of possible resource sets that can be examined in a\nreasonable time becomes smaller. Another disadvantage of these\nsystems is that the performance degradation detection is suitable\nonly for iterative or regular applications.\nCactus (2) and GridWay (14) do not use performance models.\nHowever, these frameworks are only suitable for sequential \n(GridWay) or single-site applications (Cactus). In that case, the resource\nselection problem boils down to selecting the fastest machine or\ncluster. Processor clock speed, average load and a number of \nprocessors in a cluster (Cactus) are used to rank resources and the \nresource with the highest rank is selected. The application is migrated\nif performance degradation is detected or better resources are \ndiscovered. Both Cactus and GridWay use the number of iterations per\ntime unit as the performance indicator. The main limitation of this\nmethodology is that it is suitable only for sequential or single-site\napplications. Moreover, resource selection based on clock speed is\nnot always accurate. Finally, performance degradation detection is\nsuitable only for iterative applications and cannot be used for \nirregular computations such as search and optimization problems.\nThe resource selection problem was also studied by the AppLeS\nproject (5). In the context of this project, a number of applications\nwere studied and performance models for these applications were\ncreated. Based on such a model a scheduling agent is built that\nuses the performance model to select the best resource set and the\nbest application schedule on this set. AppLeS scheduling agents are\nwritten on a case-by-case basis and cannot be reused for another \napplication. Two reusable templates were also developed for specific\nclasses of applications, namely master-worker (AMWAT template)\nand parameter sweep (APST template) applications. Migration is\nnot supported by the AppLeS software.\n127\nIn (13), the problem of scheduling master-worker applications\nis studied. The authors assume homogeneous processors (i.e., with\nthe same speed) and do not take communication costs into account.\nTherefore, the problem is reduced to finding the right number of\nworkers. The approach here is similar to ours in that no \nperformance model is used. Instead, the system tries to deduce the \napplication requirements at runtime and adjusts the number of workers\nto approach the ideal number.\n7. Conclusions and future work\nIn this paper, we investigated the problem of resource selection and\nadaptation in grid environments. Existing approaches to these \nproblems typically assume the existence of a performance model that\nallows predicting application runtimes on various sets of resources.\nHowever, creating performance models is inherently difficult and\nrequires knowledge about the application. We propose an approach\nthat does not require in-depth knowledge about the application. We\nstart the application on an arbitrary set of resources and monitor\nits performance. The performance monitoring allows us to learn\ncertain application requirements such as the number of processors\nneeded by the application or the application\"s bandwidth \nrequirements. We use this knowledge to gradually refine the resource set\nby removing inadequate nodes or adding new nodes if necessary.\nThis approach does not result in the optimal resource set, but in a\nreasonable resource set, i.e. a set free from various performance\nbottlenecks such as slow network connections or overloaded \nprocessors. Our approach also allows the application to adapt to the\nchanging grid conditions.\nThe adaptation decisions are based on the weighted average \nefficiency - an extension of the concept of parallel efficiency defined\nfor traditional, homogeneous parallel machines. If the weighted \naverage efficiency drops below a certain level, the adaptation \ncoordinator starts removing worst nodes. The badness of the nodes is\ndefined by a heuristic formula. If the weighted average efficiency\nraises above a certain level, new nodes are added. Our simple \nadaptation strategy allows us to handle multiple scenarios typical for\ngrid environments: expand to more nodes or shrink to fewer nodes\nif the application was started on an inappropriate number of \nprocessors, remove inadequate nodes and replace them with better ones,\nreplace crashed processors, etc. The application adapts fully \nautomatically to changing conditions. We implemented our approach\nin the Satin divide-and-conquer framework and evaluated it on\nthe DAS-2 distributed supercomputer and demonstrate that our \napproach can yield significant performance improvements (up to 60%\nin our experiments).\nFuture work will involve extending our adaptation strategy\nto support opportunistic migration. This, however, requires grid\nschedulers with more sophisticated functionality than currently \nexists. Further research is also needed to decrease the benchmarking\noverhead. For example, the information about CPU load could\nbe used to decrease the benchmarking frequency. Another line of\nresearch that we wish to investigate is using feedback control to\nrefine the adaptation strategy during the application run. For \nexample, the node badness formula could be refined at runtime based\non the effectiveness of the previous adaptation decisions. Finally,\nthe centralized implementation of the adaptation coordinator might\nbecome a bottleneck for applications which are running on very\nlarge numbers of nodes (hundreds or thousands). This problem can\nbe solved by implementing a hierarchy of coordinators: one \nsubcoordinator per cluster which collects and processes statistics from\nits cluster and one main coordinator which collects the information\nfrom the sub-coordinators.\nAcknowledgments\nThis work was carried out in the context of Virtual Laboratory for\ne-Science project (ww.vl-e.nl). This project is supported by a BSIK\ngrant from the Dutch Ministry of Education, Culture and Science\n(OC&W) and is part of the ICT innovation program of the Ministry\nof Economic Affairs (EZ).\nReferences\n[1] M. Aldinucci, F. Andre, J. Buisson, S. Campa, M. Coppola,\nM. Danelutto, and C. Zoccolo. Parallel program/component\nadaptivity management. In ParCo 2005, Sept. 2005.\n[2] G. Allen, D. Angulo, I. Foster, G. Lanfermann, C. Liu,\nT. Radke, E. Seidel, and J. Shalf. The cactus worm: \nExperiments with resource discovery and allocation in a grid \nenvironment. Int\"l Journal of High Performance Computing \nApplications, 15(4):345-358, 2001.\n[3] G. Allen, K. Davis, K. N. Dolkas, N. D. Doulamis, T. Goodale,\nT. Kielmann, A. Merzky, J. Nabrzyski, J. Pukacki, T. Radke,\nM. Russell, E. Seidel, J. Shalf, and I. Taylor. Enabling \napplications on the grid - a gridlab overview. Int\"l Journal of\nHigh-Performance Computing Applications, 17(4):449-466,\nAug. 2003.\n[4] J. E. Baldeschwieler, R. D. Blumofe, and E. A. Brewer. \nATLAS: An Infrastructure for Global Computing. In 7th ACM\nSIGOPS European Workshop on System Support for \nWorldwide Applications, pages 165-172, Sept. 1996.\n[5] F. Berman, R. Wolski, H. Casanova, W. Cirne, H. Dail,\nM. Faerman, S. Figueira, J. Hayes, G. Obertelli, J. Schopf,\nG. Shao, S. Smallen, N. Spring, A. Su, and D. \nZagorodnov. Adaptive Computing on the Grid Using AppLeS. IEEE\nTrans. on Parallel and Distributed Systems, 14(4):369-382,\nApr. 2003.\n[6] D.-M. Chiu, M. Kadansky, J. Provino, and J. Wesley. \nExperiences in programming a traffic shaper. In 5th IEEE Symp. on\nComputers and Communications, pages 470-476, 2000.\n[7] W. Chrabakh and R. Wolski. GridSAT: A Chaff-based \nDistributed SAT Solver for the Grid. In 2003 ACM/IEEE \nconference on Supercomputing, page 37, 2003.\n[8] The Distributed ASCI Supercomputer (DAS).\nhttp://www.cs.vu.nl/das2/.\n[9] N. Drost, R. V. van Nieuwport, and H. E. Bal. Simple \nlocalityaware co-allocation in peer-to-peer supercomputing. In 6th\nInt\"l Workshop on Global Peer-2-Peer Computing, May 2005.\n[10] D. L. Eager, J. Zahorjan, and E. D. Lazowska. Speedup\nversus efficiency in parallel systems. IEEE Transactions on\nComputers, 38(3):408-423, Mar. 1989.\n[11] I. Foster. Globus toolkit version 4: Software for \nserviceoriented systems. In IFIP International Conference on \nNetwork and Parallel Computing, pages 2-13. Springer-Verlag\nLNCS 3779, 2005.\n[12] J.-P. Goux, S. Kulkarni, M. Yoder, and J. Linderoth. An\nEnabling Framework for Master-Worker Applications on the\nComputational Grid. In 9th IEEE Int\"l Symp. on High \nPerformance Distributed Computing, pages 43-50, Aug. 2000.\n[13] E. Heymann, M. A. Senar, E. Luque, and M. Livny. \nAdaptive scheduling for master-worker applications on the \ncomputational grid. In 1st IEEE/ACM International Workshop\non Grid Computing, pages 214-227. Springer Verlag LNCS\n1971, 2000.\n128\n[14] E. Huedo, R. S. Montero, and I. M. Llorente. A framework\nfor adaptive execution in grids. Software - Practice & \nExperience, 34(7):631-651, 2004.\n[15] H. H. Mohamed and D. H. Epema. Experiences with the\nKOALA Co-Allocating Scheduler in Multiclusters. In 5th\nIEEE/ACM Int\"l Symp. on Cluster Computing and the GRID,\npages 640-650, May 2005.\n[16] A. Plaat, H. E. Bal, and R. F. H. Hofman. Sensitivity of\nparallel applications to large differences in bandwidth and\nlatency in two-layer interconnects. In 5th Int\"l Symp. On\nHigh Performance Computer Architecture, pages 244-253,\nJan. 1999.\n[17] J. W. Romein, H. E. Bal, J. Schaeffer, and A. Plaat. A \nperformance analysis of transposition-table-driven work scheduling\nin distributed search. IEEE Trans. on Parallel and Distributed\nSystems, 13(5):447-459, May 2002.\n[18] S. S. Vadhiyar and J. J. Dongarra. Self adaptivity in Grid\ncomputing. Concurrency and Computation: Practice and\nExperience, 17(2-4):235-257, 2005.\n[19] R. V. van Nieuwpoort, T. Kielmann, and H. E. Bal. Efficient\nload balancing for wide-area divide-and-conquer applications.\nIn 8th ACM SIGPLAN Symp. on Principles and Practices of\nParallel Programming, pages 34-43, 2001.\n[20] R. V. van Nieuwpoort, J. Maassen, T. Kielmann, and H. E. Bal.\nSatin: Simple and Efficient Java-based Grid Programming.\nScalable Computing: Practice and Experience, 6(3):19-32,\nSept. 2004.\n[21] R. V. van Nieuwpoort, J. Maassen, G. Wrzesinska, R. \nHofman, C. Jacobs, T. Kielmann, and H. E. Bal. Ibis: a \nFlexible and Efficient Java-based Grid Programming Environment.\nConcurrency & Computation: Practice & Experience, \n17(78):1079-1107, 2005.\n[22] R. Wolski, N. Spring, and J. Hayes. The network weather \nservice: A distributed resource performance forecasting service\nfor metacomputing. Journal of Future Generation Computing\nSystems, 15(5-6):757-768, Oct. 1999.\n[23] G. Wrzesinska, R. V. van Nieuwport, J. Maassen, and H. E.\nBal. 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{"_id":"5efb42386a7f7100184ab4dc","name":"18637.xml","content":"OPINIONBACA, Justice.1. Pursuant to Rule 12-102 NMRA 1997, Appellant Tanya Doreen Baca (\"Defendant\") seeks review of her conviction for first degree murder and tampering with evidence. On appeal, Defendant first contends that the trial court erred by excluding from trial as hearsay the grand jury testimony of a defense witness. On appeal of this issue, we examine: 1) whether the Defendant preserved error; 2) whether the hearsay ruling constituted plain error; 3) whether defense counsel's failure to object to the hearsay ruling was ineffective assistance of counsel, and; 4) whether the trial court erred when it ruled that the grand jury testimony was inadmissible under the former-testimony exception to the bar against hearsay evidence.2. The Defendant also argues that she was denied due process because of prosecutorial misconduct. With regard to this contention, we review: 1) whether the State committed prosecutorial misconduct by not granting use-immunity to a defense witness where that witness allegedly possessed knowledge important to the Defendant's case, and 2) whether the State mischaracterized the law of first degree murder during its closing such that fundamental error resulted.3. We conclude, after consideration of Defendant's arguments, that the trial court did not err by excluding the grand jury testimony from use at trial. In addition, we do not find that the Defendant was denied due process because of prosecutorial misconduct. Therefore, we find no basis for reversal and uphold the conviction.I.4. Defendant was convicted of first degree murder and tampering with evidence. The State built its case against the Defendant primarily on the testimony of alleged eye-witnesses who claimed to have seen Defendant carry out the actions leading to the charges.5. The first State witness, Eugene Throgmorton, testified that around 9 PM on November 3,1994, he accompanied Shawn Qualtire, Defendant's pimp, to Defendant's hotel room at the Rose Villa Motel in Albuquerque. When they arrived, Defendant, Bobby Hernandez, and Defendant's daughter, Ashley, were in the room.6. In the early hours of November 4, 1994, Throgmorton left the motel room with Defendant, Hernandez, and Qualtire for the purpose of purchasing drugs. Afterward, the group began walking back to the motel room. According to Throgmorton, Defendant suggested that the group take a route through an alley. Once in the alley, Defendant pulled out a pistol and ordered Qualtire to get against a wall. Defendant then shot Qualtire several times in the chest and again in the head as Qualtire lay on the ground. Throgmorton stated that he subsequently returned to the room with the Defendant and Hernandez. Defendant then packed her bags and called her mother to come pick her up. She also left the room for a brief time, stating that she wanted to dispose of the pistol and to find some gasoline to wash the gun powder from her hands.7. The State also called Roger Wahl to testify. Wahl, a transvestite prostitute, testified that the Defendant came by Wahl's room on the morning in question in search of gasoline to clean her hands. During the visit, the Defendant admitted to killing Qualtire. Wahl also claimed that the Defendant threatened to kill Wahl if he told anyone about the shooting.8. At the conclusion of the State's case, the defense called Bobby Hernandez to testify. In earlier statements to police, Hernandez told a rendition of the facts surrounding Qualtire's death that was very similar to the version given by Throgmorton. However, at trial, Hernandez testified that he had given that version of the facts under police coercion. He alleged that officers from the Albuquerque Police Department had threatened him with charges of conspiracy to commit murder if he did not give a statement similar to that of Throgmorton. Thus, Hernandez contended that his éarlier statement was a fabrication, and he concluded that he never actually saw the Defendant kill anyone.9. The Defendant also attempted to call for testimony another alleged eye-witness to the shooting, Ms. Erica Markunes O'Leary. At trial, the court offered to provide O'Leary with counsel since she was present at the scene on the night of the shooting and since her rights and interests might be affected by the testimony she was to give in the Defendant's trial. Initially, O'Leary refused the court's offer to provide her with counsel. At that time, however, the prosecution informed O'Leary that although she was not under suspicion for the murder, she might face perjury charges if her trial testimony differed substantially from that which she gave at the grand jury. O'Leary then accepted the court's offer of counsel, and upon consultation, O'Leary refused to testify, asserting her Fifth Amendment privilege.10. Since O'Leary refused to testify, the defense sought to have a tape of O'Leary's grand jury testimony entered into evidence at trial. The core story of O'Leary's testimony was similar to both that of Throgmorton and the initial story told to police by Hernandez, but there were substantial differences in her version of the facts surrounding the killing, including an allegation that Qualtire was beaten before he was shot. The defense argued that these differences strengthened the credibility of Hernandez, who had testified that the police coerced him into lying. Also, the Defendant believed that O'Leary's eventual testimony, in concert with Hernandez's testimony, would raise a reasonable doubt about the Defendant's guilt and would call into question the veracity of the State's witnesses. The trial court refused to allow the grand jury tape of O'Leary into evidence, finding it to be hearsay, inadmissible under the former-testimony exception to the general bar against such evidence.II.11. We review whether the Defendant is entitled to any relief based upon the trial court's treatment of O'Leary's grand jury testimony as hearsay. Defendant contends first that she properly preserved the trial court's alleged error for review. Second, if error was not properly preserved, the trial court's treatment of O'Leary's testimony as hearsay is reviewable as plain error. If neither of the preceding arguments applies, then defense counsel's failure to preserve the issue was ineffective assistance of counsel. Finally, Defendant contends that the trial court erred by not admitting O'Leary's testimony under the former-testimony exception to the bar against hearsay evidence. We find no reversible error in any of the Defendant's contentions and hold that she is entitled to no relief on any of the aforementioned grounds.A.12. We initially consider whether the Defendant preserved error on the issue of the hearsay status of O'Leary's grand jury testimony. Defendant now contends on appeal that O'Leary's grand jury testimony was not offered into evidence to prove the truth of the matter asserted and, as such, should not have been treated as hearsay. We hold that the Defendant failed to preserve error on this issue.13. A defendant must obtain a clear ruling from the trial court in order to preserve the grounds alleged to have been in error. State v. Lucero, 116 N.M. 450, 453, 863 P.2d 1071, 1074 (1993); State v. Garcia, 100 N.M. 120, 123, 666 P.2d 1267, 1270 (Ct.App.1983). An objection that does not state the grounds for the objection preserves no issue for appeal. Rule 11-103(A) NMRA 1997; State v. Barraza, 110 N.M. 45, 49, 791 P.2d 799, 804 (Ct.App.1990). However, the objection need not be specific if the specific ground is apparent from the context. Barraza, 110 N.M. at 49, 791 P.2d at 799.14. In Garcia, the defendant was convicted on charges of criminal sexual penetration and kidnapping. Garcia, 100 N.M. at 122, 666 P.2d at 1269. On appeal of the conviction, the defendant sought review of whether exclusion of certain evidence as hearsay was proper. Id. at 123, 666 P.2d at 1268. However, the defendant failed to bring his hearsay concerns to the attention of the trial judge, and the defendant also failed to make an offer of proof in order to include the issue within the record. Id. Therefore, the court of appeals refused to consider the hearsay arguments included by the defendant in his petition for review. Id.15. Similarly, the Defendant in the immediate ease has not preserved the issue of whether the grand jury testimony of O'Leary constituted hearsay. The record indicates that defense counsel tendered the evidence and requested that the trial court \"be fair\" to his client and admit the grand jury testimony. (Tr. VI, 103) However, aside from this reference, the defense made no specific argument that the testimony should be admitted because it was not hearsay; counsel did not assert that O'Leary's testimony was not being offered for the truth of the matter asserted. In fact, the record indicates that counsel moved immediately to a discussion of exceptions to the hearsay rule as a basis for admission of the grand jury testimony. The context of these arguments would not have put the court on notice that the defense objected to treatment of the testimony as hearsay. Therefore, defense counsel's statements did not preserve the alleged error for review by this Court.B.16. The Defendant also argues that if error was not preserved, then the trial court's exclusion of O'Leary's grand jury testimony constituted plain error. According to Defendant's reasoning, O'Leary's grand jury testimony was not hearsay because it was not offered to prove the truth of the matter asserted. Instead, it was intended to cast doubt on the veracity of the State's witnesses and support the contention that the police created a story and pressured witnesses to adhere to that story. Defendant now argues that the trial court erred by denying admission of O'Leary's grand jury testimony, substantially affecting Defendant's ability to present her full defense. We disagree.17. To the extent the record does not provide a clear basis for appellate review on the grounds the Defendant now asserts, Defendant must show plain error affecting a substantial right. Rule 11-103(D) NMRA 1997; Lucero, 116 N.M. at 453, 863 P.2d at 1074. The court must be convinced that the evidentiary decision constituted an injustice that creates grave doubts concerning the validity of the verdict. State v. Contreras, 1995 NMSC 064, 23, 120 N.M. 486, 903 P.2d 228; Barraza, 110 N.M. at 49, 791 P.2d at 803.18. We hold that the Defendant is not entitled to relief under the plain error rule. Although the exclusion might have limited the theory of the defense somewhat, we are not convinced that the Defendant's right to put forth her defense was substantially hampered. Counsel had the opportunity on cross examination to explore the defense's theory that State witnesses were lying and had been pressured by the police to fabricate testimony. Furthermore, to bolster the theory that witnesses were lying, counsel had ample occasion to cite Hernandez's declaration that he had lied to police in his initial interviews and to seek admission of Throgmorton's conviction for armed robbery. Thus, the trial court's exclusion of O'Leary's testimony as hearsay did not affect a substantial right because the defense had several opportunities to present its theory through other avenues.19. In addition, the exclusion of this evidence does not taint the validity of the verdict rendered in this case. O'Leary ultimately concluded in her grand jury testimony that she in fact saw the Defendant shoot and kill Qualtire. While there were differences between her grand jury testimony and the trial testimony of Throgmorton, these differences do not warrant relief, especially when the conclusion of the excluded evidence on the ultimate issue in this case supports the conviction.C.20. We next review whether the failure by Defendant's attorney to preserve error on the hearsay issue constituted ineffective assistance of counsel. To succeed on a claim of ineffective assistance of counsel, a defendant must demonstrate that his counsel did not exercise the skill of a reasonably competent attorney, State v. Orona, 97 N.M. 232, 233, 638 P.2d 1077, 1078 (1982), and the defendant must also prove that the incompetent representation prejudiced the defendant's case, rendering the trial court's results unreliable. State v. Lopez, 1996 NMSC 036, 25, 122 N.M. 63, 920 P.2d 1017. The main question is whether the allegedly incompe tent representation prejudiced the case such that but for counsel's error, there is a reasonable probability that the result of the conviction proceedings would have been different. Lopez, 1996 NMSC 036, 26, 122 N.M. 63, 920 P.2d 1017.21. We already determined that the trial court's decision to exclude O'Leary's testimony as hearsay did not affect any of the Defendant's substantial rights. Without proof of such prejudice, Defendant's claim of ineffective assistance of counsel fails, and there is no need to address whether her counsel exercised the skill of a reasonably competent attorney.D.22. We also review whether the trial court erred by refusing to admit O'Leary's grand jury testimony under the former-testimony exception to the bar against hearsay evidence. The admission or exclusion of evidence is entrusted to the discretion of the trial court; its ruling will not be disturbed absent abuse of discretion. State v. Smith, 104 N.M. 329, 333-34, 721 P.2d 397, 401-02 (1986), overruled on other grounds by Gallegos v. Citizens Insurance Agency, 108 N.M. 722, 731, 779 P.2d 99, 108 (1989); State v. Stephens, 99 N.M. 32, 37, 653 P.2d 863, 868 (1982). If the court erred in denying the Defendant use of the evidence, no relief is warranted unless the Defendant also shows a reasonable probability the ruling contributed to his conviction. State v. Jett, 111 N.M. 309, 312, 805 P.2d 78, 81 (1991). We conclude that there was no abuse of discretion, and the Defendant has not demonstrated a reasonable probability that the ruling contributed to her conviction. Therefore, we uphold the trial court's decision to exclude the testimony.23. Hearsay is a statement, other than one made by the declarant while testifying at the trial or hearing, offered in evidence to prove the truth of the matter asserted. Rule 11-801(0 NMRA 1997. Generally, hearsay evidence is inadmissible, except as provided for under specific exception. Rule 11-802 NMRA 1997. One such exception is Rule 11 — 804(B)(1) NMRA 1997. It admits into evidencé the former-testimony of a currently unavailable witness as an exception to the hearsay rule, and it states in relevant part:Former testimony. Testimony given as a witness at another hearing of the same or a different proceeding or in a deposition taken in compliance with law in the course of the same or another proceeding, if the party against whom the testimony is now offered ... had an opportunity and similar motive to develop the testimony by direct, cross or redirect examination.Id,24. Once O'Leary invoked her Fifth Amendment privilege, the trial court correctly ruled that she was an unavailable witness. United States v. Salerno, 505 U.S. 317, 112 S.Ct. 2503, 120 L.Ed.2d 255 (1992) (holding that a witness who invokes the right against self-incrimination is considered unavailable within the hearsay rules); State v. Gonzales, 1992 NMSC 002, 15, 113 N.M. 221, 225, 824 P.2d 1023, 1027 (same). In determining the applicability of the former-testimony hearsay exception, the relevant inquiry then became whether the State, as the party against whom O'Leary's testimony was to be offered, had an opportunity and similar motive to develop the testimony at the grand jury hearing as it would have had at trial. Defendant argued at trial, and now contends on appeal, that the prosecution had a similar motive at the grand jury hearing to develop O'Leary's testimony.25. The particular facts of the Defendant's case suggest that the State's motives in developing O'Leary's testimony at the grand jury were very different than what they would have been at trial. O'Leary claimed in her grand jury testimony that Qualtire had been beaten before he was shot. However, at the time of the grand jury hearing, the medical report on Qualtire was not yet available. When the report was finished, it did not include any information suggesting that Qualtire had been beaten, as O'Leary had testified. Instead, the report showed that, consistent with the testimony of Throgmorton.and Hernandez's early statements to police, Qualtire had been shot in the abdomen three times and then shot twice in the head as he lay on the, ground.26. The prosecutor did not know at the grand jury hearing that O'Leary's testimony was going to be inconsistent with the medical evidence. Nor did the prosecutor know that other eye-witness accounts would be consistent with the medical evidence and inconsistent with O'Leary's testimony. However, because the medical information was not available prior to the grand jury hearing, the State had no reason to question O'Leary's veracity or to probe her claim that Qualtire was beaten before he was killed. See People v. Radovick, 275 Ill.App.3d 809, 212 Ill.Dec. 82, 88, 656 N.E.2d 235, 241 (1995) (finding no abuse of discretion by trial court for excluding grand jury testimony because the State is developing its case and \"would have had Httle motive to ehaHenge [a witness's] version of events at this time\"). In addition, the State had no reason to question O'Leary on the Defendant's eventual theory that O'Leary and other eye-witnesses were coerced by the police to lie. Thus, the State's motives in developing O'Leary's testimony were altered substantiaUy between the grand jury hearing and the time of trial.27. This holding is supported by the substantial differences between a grand jury hearing and trial. First, the procedural variations between the two proceedings suggest several practical limitations on the State's abHity to develop testimony at the grand jury hearing. O'Leary testified before a grand jury, and the State was obligated to present any potentiaUy exculpatory evidence in the case. NMSA 1978, 31-6-11 (1981). Also, the grand jury system is not run under the same technical, procedural, and evidentiary rules governing the conduct of criminal trials. Buzbee v. Donnelly, 96 N.M. 692, 696, 634 P.2d 1244, 1248 (1981).28. Second, the posture of the grand jury hearing is markedly different from that of trial. Criminal trials tend to be highly confrontational. Conversely, the prosecutor's duty with the grand jury is to protect both the interests of the public and the rights of the accused. Matter of Grand Jury of Sandoval County, 106 N.M. 764, 768, 750 P.2d 464, 468 (Ct.App.1988). The prosecutor is not trying to prove any side of any issue, but only to develop the facts to determine if an indictment is warranted. United States v. DiNapoli, 8 F.3d 909, 915 (2nd Cir.1993). Such differences in posture and objectives, when considered with the facts avaüable at the time of the grand jury hearing, lead us to decide, like the trial court, that the State's motives in developing O'Leary's testimony were very different than what they would have been at trial.29. Defendant primarily relies on United States v. Miller, 904 F.2d 65 (D.C.Cir.1990) and United States v. Salerno, 974 F.2d 231 (2nd Cir.1992), to support her position that O'Leary's grand jury testimony should have been admitted under the former-testimony exception. This rebanee is misplaced. Defendant correctly points out that in Miller the appellate court permitted the admission of grand jury testimony under the former-testimony exception. Miller, 904 F.2d at 67-68. However, the court of appeals noted that the government's approach in questioning the witness did not differ at trial. Id. at 68 n. 3. Conversely, in the immediate ease the motives of the State in questioning witnesses changed between the grand jury hearing and trial. Although the State might have possessed an interest at the grand jury hearing in obtaining testimony as to exact dates, times, and actions, the nature of the evidence available at the time was not such that the State would have had reason to question the accuracy of O'Leary's story. Hence, the State's posture toward O'Leary's testimony would have changed substantially after the medical report became avaüable.30. Defendant's reliance on Salerno is also misplaced. Defendant correctly asserts that after remand by the Supreme Court, the Second Circuit in Salerno found the government had a similar motive to examine the witnesses and the district court erred in excluding the grand jury testimony. Salerno, 974 F.2d at 240. In the immediate case, however, the State did not examine O'Leary at the grand jury hearing as it would have at trial and did not have the same motive to do so. More significantly, the Second Circuit en bane vacated the opinion rehed upon by the Defendant and reversed its holding. DiNapoli 8 F.3d at 915. In reversing, the court found that at the grand jury, the prosecutor had no interest in proving the falsity of the witness. Id. The prosecutor had a different motive at the grand jury proceeding, and as such, he had no motive to cross-examine the witnesses to show that their testimony was false. Id. Therefore, the district court was correct in excluding the grand jury testimony. Id.31. In sum, substantial changes in the status of evidence and the lack of motive for the State to rigorously examine O'Leary support the trial court's decision to exclude the grand jury testimony. Furthermore, even if exclusion of the testimony had been deemed to be error, we are not convinced, as discussed earlier, that the exclusion in any way prejudiced the Defendant.III.32. We now review whether prosecutorial misconduct denied the Defendant a fair trial. Defendant contends first that the trial court should have granted use-immunity to O'Leary since the prosecution improperly refused to request it. Second, Defendant argues that portions of the prosecutor's closing statement constituted prosecutorial misconduct.A.33. Defendant alleges that the prosecution improperly refused to grant use-immunity to O'Leary for Defendant's trial, knowing that O'Leary would invoke her Fifth Amendment privilege. Defendant contends that this was a deliberate attempt by the prosecution to suppress the truth by keeping O'Leary's testimony out, and for these reasons, the trial court violated Defendant's due process rights to compulsory process by refusing to extend use-immunity to O'Leary. We disagree.34. In this case, when O'Leary first appeared before the trial court, she was offered an attorney to advise her, but she refused, stating that she did not believe that she needed one. The prosecution then informed her that she could face perjury charges if there were differences between her grand jury testimony and the statements that she was to give now before the court. O'Leary then accepted the court's offer of representation, and after consultation with her attorney, O'Leary invoked her Fifth Amendment privilege.35. Defendant correctly notes that a witness cannot be encouraged or badgered not to testify. United States v. Arthur, 949 F.2d 211, 215-16 (6th Cir.1991). However, the prosecutor and the court should advise a witness that testimony which differs from prior sworn testimony may give rise to perjury charges. United States v. Smith, 997 F.2d 674, 680 (10th Cir.1993). It is not improper to merely advise a witness that he could face prosecution for perjury. Id.36. There is no indication in the record that O'Leary was pressured or unduly discouraged from testifying. After consultation with an appointed independent counsel, O'Leary elected to invoke her Fifth Amendment privilege. The other alternative would have been to not warn O'Leary of the possible consequences that she might face for inconsistent testimony. This warning was correct procedure here, and we note nothing threatening or coercive about it. As such, no basis for error can be premised on the State's warning to O'Leary in this instance.37. Furthermore, we do not believe that the court was required to offer use-immunity to O'Leary. Under Rule 5-116 NMRA 1997, New Mexico courts may recog-' nize use-immunity upon written application of the prosecuting attorney. However, there is no constitutional provision or statute in New Mexico conferring a right to use-immunity for a defense witness, and a trial court has no power to confer immunity absent a written application from the State. State v. Cheadle, 101 N.M. 282, 286-87, 681 P.2d 708, 712-13 (1983); State v. Sanchez, 98 N.M. 428, 431-34, 649 P.2d 496, 499-502 (Ct.App.1982). In Sanchez, the court of appeals noted:A defendant has no sixth amendment right to demand that any witness he chooses be immunized, and the prosecution's refusal to grant immunity to a defense witness who would allegedly offer exculpatory testimony to a defendant does not amount to a denial of due process or a violation of appellant's sixth amendment rights.98 N.M. at 434, 649 P.2d at 502. This finding has been supported by the Tenth Circuit. See McGee v. Crist, 739 F.2d 505, 509 (10th Cir.1984) (finding that only the prosecutor may seek witness immunity).38. We are not convinced that the authorities cited by the Defendant undermine this finding. Defendant relies primarily on Government of Virgin Islands v. Smith, 615 F.2d 964, 972-73 (3rd Cir.1980), for the contention that a judge may order witness immunity in limited circumstances if the testimony is properly sought in district court, clearly exculpatory, essential to the defendant's case, and not opposed by a strong countervailing government interest. As we have discussed in earlier sections, we do not believe that O'Leary's excluded testimony was clearly exculpatory. Furthermore, the Smith rationale is not consistent with the weight of fedei'al authority on this issue. See, e.g., United States v. Hunter, 672 F.2d 815, 818 (10th Cir.1982) (holding that the federal district court has no authority to grant immunity or to demand that the government seek immunity for a defense witness); United States v. Capozzi, 883 F.2d 608, 613 (8th Cir.1989) (same).39. More importantly for our analysis, this Court specifically rejected the Smith rationale. Cheadle, 101 N.M. at 287, 681 P.2d at 713 (stating that \"there is no authority to demand immunity for a witness by the defense\"). Therefore, we hold that, barring a clear showing of prosecutorial misconduct, use-immunity can only be sought by the prosecution. As noted, the warning issued to O'Leary by the prosecutor did not constitute misconduct by the State, and therefore, we deny relief on this claim.B.40. Finally, we review whether the prosecutor committed misconduct in his closing by allegedly misstating the law on an element of the charged crime. In his closing, the prosecutor discussed deliberation as an element of first degree murder using the following examples:How long does it take to make a decision? Would you like french fries or baked potato? French fries. Ma'am, would that be wheat bread or white? Do you want to go or do you want to stay? I'll go. Alls [sic] it takes is the ability to say this is the result of my action. Is that the result I want? That's deliberation.And the court tells you [that] you can see that in a very short period of time. It doesn't take sitting around with a council of elders or a little lap top computer and everything. We're dealing with the human mind. Do you stop or go?The evidence in this case shows that it was premeditated and intentional.Part of the New Mexico jury instructions for first degree murder explain that deliberate intent is \"a result of careful thought and the weighing of the considerations for and against the proposed course of action.\" Rule 14-201 NMRA1997. These instructions also note that this process might take very little time, but also that it involves more than an uneonsidered and rash impulse. Id. Defendant contends that the prosecutor's discussion of the specific intent element of first degree murder misled the jury and prejudiced the Defendant's right to a fair trial. We conclude that there is no basis for granting relief on this claim.41. Defendant's attorney neglected to object to the alleged error during closing. Without such an objection during summation, appellate review is for fundamental error. State v. Apodaca, 1994 NMSC 121, 20, 118 N.M. 762, 769-70, 887 P.2d 756, 763-64. Fundamental error only applies in exceptional circumstances when guilt is so doubtful that it would shock the judicial conscience to allow the conviction to stand. State v. Aguilar, 117 N.M. 501, 507, 873 P.2d 247, 253 (1994).42. A prosecutor's summation which misstates the law invades the province of the court. State v. Gonzales, 105 N.M. 238, 242, 731 P.2d 381, 385 (Ct.App.1986); State v. Diaz, 100 N.M. 210, 214-15, 668 P.2d 326, 330-31 (Ct.App.1983). However, a prosecutor is allowed latitude in summation. State v. Gonzales, 113 N.M. 221, 229, 824 P.2d 1023, 1031 (1992); State v. Chamberlain, 112 N.M. 723, 729, 819 P.2d 673, 679 (1991). This Court reviews comments made in closing argument in the context in which they occurred so that the court may gain full understanding of the comments and their potential effect on the jury. State v. Armendarez, 113 N.M. 335, 338, 825 P.2d 1245, 1248 (1992).43. In Armendarez, this Court considered a fundamental error claim very similar to that which Defendant argues in the immediate case. Armendarez was convicted of first degree murder. Id. at 336, 825 P.2d at 1246. On appeal, he argued that the prosecutor had included an incorrect description of the law in his closing and that the State was attempting to \"instruct\" the jury. Id. at 338, 825 P.2d at 1248. This Court rejected the defendant's arguments, finding that the prosecutor was merely attempting to explain the law to the jury, just as defense counsel had. Id. Furthermore, the court gave the jury direct instructions regarding the elements of the crime, and we found that \"the jury followed the written instructions and did not rely for its verdict on one very brief part of the State's closing remarks.\" Id. The State's conduct fell well short of fundamental error. Id.44. Similarly, we conclude that the State's closing remarks in this case did not constitute fundamental error. As with Armendarez, the comments were a very brief part of a lengthy summation, and we believe that the prosecutor's statements were used merely as an illustration of the process one uses to weigh the considerations of a contemplated course of action. We conclude that the State did not in any way suggest to the jury that it should apply the law in a manner different, than that prescribed by the judge.45. It is also worth noting that the trial court gave the jury a clear and direct explanation of the law before deliberations began. The court instructed the jury in the presumption of the Defendant's innocence, the State's burden to prove guilt beyond a reasonable doubt, and the elements of the crime. As we decided in Armendarez, we are not willing to assume that the jury took the comment during closing and applied it as the law governing the case, ignoring the instructions given by the court. Id. We see no evidence of prejudice warranting such a conclusion. Ample evidence was presented to support a finding of deliberation, including testimony on the Defendant's procurement of the weapon, her suggestion that the group use the alley, the wounding of Qualtire, and the firing of the fatal gunshots to the head. There are no exceptional circumstances in this case indicating a wrongful conviction based on an unwarranted or incorrect finding of deliberation. As such, we conclude that no fundamental error occurred.III.46. In sum, we hold that the trial court did not err in excluding O'Leary's grand jury testimony from the trial. Furthermore, the record does not indicate that the Defendant's due process rights were violated by prosecutorial misconduct. For these reasons, we AFFIRM.47. IT IS SO ORDERED.FRANCHINI, C.J., and SERNA, J., concur.","annotations":[{"_id":"5f064fcbc3627b001895b55c","role":"Outcome","content":"For these reasons, we AFFIRM.","class":"task-bg-sentence-classification-1","index":1,"contentIdx":{"start":32027,"end":32056}},{"_id":"5f064fcbc3627b001895b55d","role":"Affirm"}]} |
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"le\u00b7sen", ","], "token_info": ["word", "punct", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["VVFIN", "$,", "PWAV", "PPER", "APPR", "ART", "NN", "VVINF", "$,"], "meter": "-+-+--+--", "measure": "iambic.tri.relaxed"}, "line.4": {"text": "Ein Poltergeist.", "tokens": ["Ein", "Pol\u00b7ter\u00b7geist", "."], "token_info": ["word", "word", "punct"], "pos": ["ART", "NN", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.2": {"line.1": {"text": "Ein Fr\u00e4ulein ists, halb Weib halb Drache,", "tokens": ["Ein", "Fr\u00e4u\u00b7lein", "ists", ",", "halb", "Weib", "halb", "Dra\u00b7che", ","], "token_info": ["word", "word", "word", "punct", "word", "word", "word", "word", "punct"], "pos": ["ART", "NN", "VAFIN", "$,", "ADJD", "NN", "ADJD", "NN", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Aus Schwabenland,", "tokens": ["Aus", "Schwa\u00b7ben\u00b7land", ","], "token_info": ["word", "word", "punct"], "pos": ["APPR", "NN", "$,"], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Von einer b\u00f6sen Fey aus Rache", "tokens": ["Von", "ei\u00b7ner", "b\u00f6\u00b7sen", "Fey", "aus", "Ra\u00b7che"], "token_info": ["word", "word", "word", "word", "word", "word"], "pos": ["APPR", "ART", "ADJA", "NN", "APPR", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Hieher gebannt.", "tokens": ["Hie\u00b7her", "ge\u00b7bannt", "."], "token_info": ["word", "word", "punct"], "pos": ["ADV", "VVPP", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.3": {"line.1": {"text": "Es liegen hunderttausend Kronen", "tokens": ["Es", "lie\u00b7gen", "hun\u00b7dert\u00b7tau\u00b7send", "Kro\u00b7nen"], "token_info": ["word", "word", "word", "word"], "pos": ["PPER", "VVFIN", "CARD", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Bereit, den Held,", "tokens": ["Be\u00b7reit", ",", "den", "Held", ","], "token_info": ["word", "punct", "word", "word", "punct"], "pos": ["NN", "$,", "ART", "NN", "$,"], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Der sie erl\u00f6set, zu belohnen.", "tokens": ["Der", "sie", "er\u00b7l\u00f6\u00b7set", ",", "zu", "be\u00b7loh\u00b7nen", "."], "token_info": ["word", "word", "word", "punct", "word", "word", "punct"], "pos": ["ART", "PPER", "VVFIN", "$,", "PTKZU", "VVINF", "$."], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Ein sch\u00f6nes Geld!", "tokens": ["Ein", "sch\u00f6\u00b7nes", "Geld", "!"], "token_info": ["word", "word", "word", "punct"], "pos": ["ART", "ADJA", "NN", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.4": {"line.1": {"text": "Zehn Ritter hatten sich verb\u00fcrget,", "tokens": ["Zehn", "Rit\u00b7ter", "hat\u00b7ten", "sich", "ver\u00b7b\u00fcr\u00b7get", ","], "token_info": ["word", "word", "word", "word", "word", "punct"], "pos": ["CARD", "NN", "VAFIN", "PRF", "VVFIN", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Sie zu befreyn;", "tokens": ["Sie", "zu", "be\u00b7freyn", ";"], "token_info": ["word", "word", "word", "punct"], "pos": ["PPER", "PTKZU", "VVINF", "$."], "meter": "+--+", "measure": "iambic.di.chol"}, "line.3": {"text": "Allein man fand sie all erw\u00fcrget", "tokens": ["Al\u00b7lein", "man", "fand", "sie", "all", "er\u00b7w\u00fcr\u00b7get"], "token_info": ["word", "word", "word", "word", "word", "word"], "pos": ["ADV", "PIS", "VVFIN", "PPER", "PIAT", "VVFIN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Im nahen Hayn.", "tokens": ["Im", "na\u00b7hen", "Hayn", "."], "token_info": ["word", "word", "word", "punct"], "pos": ["APPRART", "ADJA", "NN", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.5": {"line.1": {"text": "Pipin, ein Knapp aus Lotharingen,", "tokens": ["Pi\u00b7pin", ",", "ein", "Knapp", "aus", "Lo\u00b7tha\u00b7rin\u00b7gen", ","], "token_info": ["word", "punct", "word", "word", "word", "word", "punct"], "pos": ["NE", "$,", "ART", "NN", "APPR", "NN", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Der Kempen Schmuck,", "tokens": ["Der", "Kem\u00b7pen", "Schmuck", ","], "token_info": ["word", "word", "word", "punct"], "pos": ["ART", "NN", "NN", "$,"], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Von dem noch heut die Ammen singen,", "tokens": ["Von", "dem", "noch", "heut", "die", "Am\u00b7men", "sin\u00b7gen", ","], "token_info": ["word", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["APPR", "ART", "ADV", "ADV", "ART", "NN", "VVINF", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Vernahm den Spuck.", "tokens": ["Ver\u00b7nahm", "den", "Spuck", "."], "token_info": ["word", "word", "word", "punct"], "pos": ["VVFIN", "ART", "NN", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.6": {"line.1": {"text": "Nun kann er weder ruhn noch rasten;", "tokens": ["Nun", "kann", "er", "we\u00b7der", "ruhn", "noch", "ras\u00b7ten", ";"], "token_info": ["word", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["ADV", "VMFIN", "PPER", "KON", "VVFIN", "ADV", "VVFIN", "$."], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Er steigt zu Ro\u00df,", "tokens": ["Er", "steigt", "zu", "Ro\u00df", ","], "token_info": ["word", "word", "word", "word", "punct"], "pos": ["PPER", "VVFIN", "APPR", "NN", "$,"], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Und k\u00f6mmt am Abend vor Frohnfasten", "tokens": ["Und", "k\u00f6mmt", "am", "A\u00b7bend", "vor", "Frohn\u00b7fas\u00b7ten"], "token_info": ["word", "word", "word", "word", "word", "word"], "pos": ["KON", "VVFIN", "APPRART", "NN", "APPR", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Zum Zauberschlo\u00df.", "tokens": ["Zum", "Zau\u00b7ber\u00b7schlo\u00df", "."], "token_info": ["word", "word", "punct"], "pos": ["APPRART", "NN", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.7": {"line.1": {"text": "Er setzt, ermattet von der Reise,", "tokens": ["Er", "setzt", ",", "er\u00b7mat\u00b7tet", "von", "der", "Rei\u00b7se", ","], "token_info": ["word", "word", "punct", "word", "word", "word", "word", "punct"], "pos": ["PPER", "VVFIN", "$,", "VVFIN", "APPR", "ART", "NN", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Am Thor sich hin:", "tokens": ["Am", "Thor", "sich", "hin", ":"], "token_info": ["word", "word", "word", "word", "punct"], "pos": ["APPRART", "NN", "PRF", "PTKVZ", "$."], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Drey Uhus und drey Flederm\u00e4use", "tokens": ["Drey", "U\u00b7hus", "und", "drey", "Fle\u00b7der\u00b7m\u00e4u\u00b7se"], "token_info": ["word", "word", "word", "word", "word"], "pos": ["CARD", "NN", "KON", "CARD", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Begr\u00fc\u00dfen ihn.", "tokens": ["Be\u00b7gr\u00fc\u00b7\u00dfen", "ihn", "."], "token_info": ["word", "word", "punct"], "pos": ["VVFIN", "PPER", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.8": {"line.1": {"text": "Kaum schlug es zw\u00f6lf in der Kapelle,", "tokens": ["Kaum", "schlug", "es", "zw\u00f6lf", "in", "der", "Ka\u00b7pel\u00b7le", ","], "token_info": ["word", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["ADV", "VVFIN", "PPER", "CARD", "APPR", "ART", "NN", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "So barst das Thor:", "tokens": ["So", "barst", "das", "Thor", ":"], "token_info": ["word", "word", "word", "word", "punct"], "pos": ["ADV", "ADV", "ART", "NN", "$."], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Ein gro\u00dfer Hund lag vor der Schwelle,", "tokens": ["Ein", "gro\u00b7\u00dfer", "Hund", "lag", "vor", "der", "Schwel\u00b7le", ","], "token_info": ["word", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["ART", "ADJA", "NN", "VVFIN", "APPR", "ART", "NN", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Schwarz wie ein Mohr.", "tokens": ["Schwarz", "wie", "ein", "Mohr", "."], "token_info": ["word", "word", "word", "word", "punct"], "pos": ["NE", "KOKOM", "ART", "NN", "$."], "meter": "+--+", "measure": "iambic.di.chol"}}, "stanza.9": {"line.1": {"text": "Was thut der Knapp? Er macht in Eile", "tokens": ["Was", "thut", "der", "Knapp", "?", "Er", "macht", "in", "Ei\u00b7le"], "token_info": ["word", "word", "word", "word", "punct", "word", "word", "word", "word"], "pos": ["PWS", "VVFIN", "ART", "NN", "$.", "PPER", "VVFIN", "APPR", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Das Kreutz auf ihn;", "tokens": ["Das", "Kreutz", "auf", "ihn", ";"], "token_info": ["word", "word", "word", "word", "punct"], "pos": ["ART", "NN", "APPR", "PPER", "$."], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Husch! flieht das Thier mit Angstgeheule,", "tokens": ["Husch", "!", "flieht", "das", "Thier", "mit", "Angst\u00b7ge\u00b7heu\u00b7le", ","], "token_info": ["word", "punct", "word", "word", "word", "word", "word", "punct"], "pos": ["NE", "$.", "VVFIN", "ART", "NN", "APPR", "NN", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Gott wei\u00df, wohin?", "tokens": ["Gott", "wei\u00df", ",", "wo\u00b7hin", "?"], "token_info": ["word", "word", "punct", "word", "punct"], "pos": ["NN", "VVFIN", "$,", "PWAV", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.10": {"line.1": {"text": "Nun f\u00fchrt ein Irrwisch ihn zum Throne,", "tokens": ["Nun", "f\u00fchrt", "ein", "Irr\u00b7wisch", "ihn", "zum", "Thro\u00b7ne", ","], "token_info": ["word", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["ADV", "VVFIN", "ART", "NN", "PPER", "APPRART", "NN", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Wo Bertha sa\u00df,", "tokens": ["Wo", "Bert\u00b7ha", "sa\u00df", ","], "token_info": ["word", "word", "word", "punct"], "pos": ["PWAV", "NE", "VVFIN", "$,"], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Bekr\u00e4nzt mit einer Grafenkrone", "tokens": ["Be\u00b7kr\u00e4nzt", "mit", "ei\u00b7ner", "Gra\u00b7fen\u00b7kro\u00b7ne"], "token_info": ["word", "word", "word", "word"], "pos": ["VVPP", "APPR", "ART", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Von Chrysopras.", "tokens": ["Von", "Chry\u00b7so\u00b7pras", "."], "token_info": ["word", "word", "punct"], "pos": ["APPR", "NE", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.11": {"line.1": {"text": "Bla\u00df, wie der Nix, erschien die Dirne:", "tokens": ["Bla\u00df", ",", "wie", "der", "Nix", ",", "er\u00b7schien", "die", "Dir\u00b7ne", ":"], "token_info": ["word", "punct", "word", "word", "word", "punct", "word", "word", "word", "punct"], "pos": ["NN", "$,", "PWAV", "ART", "NE", "$,", "VVFIN", "ART", "NN", "$."], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Ihr Augenpaar", "tokens": ["Ihr", "Au\u00b7gen\u00b7paar"], "token_info": ["word", "word"], "pos": ["PPOSAT", "NN"], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "War gr\u00fcn, ein Vorgeb\u00fcrg die Stirne,", "tokens": ["War", "gr\u00fcn", ",", "ein", "Vor\u00b7ge\u00b7b\u00fcrg", "die", "Stir\u00b7ne", ","], "token_info": ["word", "word", "punct", "word", "word", "word", "word", "punct"], "pos": ["VAFIN", "ADJD", "$,", "ART", "NN", "ART", "NN", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Und fahl ihr Haar.", "tokens": ["Und", "fahl", "ihr", "Haar", "."], "token_info": ["word", "word", "word", "word", "punct"], "pos": ["KON", "VVFIN", "PPOSAT", "NN", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.12": {"line.1": {"text": "Der Junker stutzt, blickt nach dem Schatze,", "tokens": ["Der", "Jun\u00b7ker", "stutzt", ",", "blickt", "nach", "dem", "Schat\u00b7ze", ","], "token_info": ["word", "word", "word", "punct", "word", "word", "word", "word", "punct"], "pos": ["ART", "NN", "VVFIN", "$,", "VVFIN", "APPR", "ART", "NN", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Und bey dem Glanz", "tokens": ["Und", "bey", "dem", "Glanz"], "token_info": ["word", "word", "word", "word"], "pos": ["KON", "APPR", "ART", "NN"], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Des Golds, vergi\u00dft er Berthens Fratze", "tokens": ["Des", "Golds", ",", "ver\u00b7gi\u00dft", "er", "Bert\u00b7hens", "Frat\u00b7ze"], "token_info": ["word", "word", "punct", "word", "word", "word", "word"], "pos": ["ART", "NN", "$,", "VVFIN", "PPER", "NE", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Und Schuppenschwanz.", "tokens": ["Und", "Schup\u00b7pen\u00b7schwanz", "."], "token_info": ["word", "word", "punct"], "pos": ["KON", "NN", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.13": {"line.1": {"text": "Wie soll ich dir die Freyheit geben,", "tokens": ["Wie", "soll", "ich", "dir", "die", "Frey\u00b7heit", "ge\u00b7ben", ","], "token_info": ["word", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["PWAV", "VMFIN", "PPER", "PPER", "ART", "NN", "VVINF", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Du holde Magd?", "tokens": ["Du", "hol\u00b7de", "Magd", "?"], "token_info": ["word", "word", "word", "punct"], "pos": ["PPER", "ADJA", "NN", "$."], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Sprach er: Mit Freuden sey mein Leben", "tokens": ["Sprach", "er", ":", "Mit", "Freu\u00b7den", "sey", "mein", "Le\u00b7ben"], "token_info": ["word", "word", "punct", "word", "word", "word", "word", "word"], "pos": ["NN", "PPER", "$.", "APPR", "NN", "VAFIN", "PPOSAT", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Daran gewagt!", "tokens": ["Da\u00b7ran", "ge\u00b7wagt", "!"], "token_info": ["word", "word", "punct"], "pos": ["PAV", "VVPP", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.14": {"line.1": {"text": "Ein Ku\u00df erl\u00f6st mich, sprach die Holde", "tokens": ["Ein", "Ku\u00df", "er\u00b7l\u00f6st", "mich", ",", "sprach", "die", "Hol\u00b7de"], "token_info": ["word", "word", "word", "word", "punct", "word", "word", "word"], "pos": ["ART", "NN", "VVFIN", "PPER", "$,", "VVFIN", "ART", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Vom Schlangenleib,", "tokens": ["Vom", "Schlan\u00b7gen\u00b7leib", ","], "token_info": ["word", "word", "punct"], "pos": ["APPRART", "NN", "$,"], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Und schenkt mich dir mit diesem Golde", "tokens": ["Und", "schenkt", "mich", "dir", "mit", "die\u00b7sem", "Gol\u00b7de"], "token_info": ["word", "word", "word", "word", "word", "word", "word"], "pos": ["KON", "VVFIN", "PPER", "PPER", "APPR", "PDAT", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Zum Eheweib.", "tokens": ["Zum", "E\u00b7he\u00b7weib", "."], "token_info": ["word", "word", "punct"], "pos": ["APPRART", "NN", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.15": {"line.1": {"text": "Er naht sich ihr mit k\u00fchner Hitze;", "tokens": ["Er", "naht", "sich", "ihr", "mit", "k\u00fch\u00b7ner", "Hit\u00b7ze", ";"], "token_info": ["word", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["PPER", "VVFIN", "PRF", "PPER", "APPR", "ADJA", "NN", "$."], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Ha! welch ein Graus!", "tokens": ["Ha", "!", "welch", "ein", "Graus", "!"], "token_info": ["word", "punct", "word", "word", "word", "punct"], "pos": ["ITJ", "$.", "PWAT", "ART", "NN", "$."], "meter": "+--+", "measure": "iambic.di.chol"}, "line.3": {"text": "Ihr Mund speit pl\u00f6tzlich rothe Blitze", "tokens": ["Ihr", "Mund", "speit", "pl\u00f6tz\u00b7lich", "ro\u00b7the", "Blit\u00b7ze"], "token_info": ["word", "word", "word", "word", "word", "word"], "pos": ["PPOSAT", "NN", "VVFIN", "ADJD", "ADJA", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Und Donner aus.", "tokens": ["Und", "Don\u00b7ner", "aus", "."], "token_info": ["word", "word", "word", "punct"], "pos": ["KON", "NN", "PTKVZ", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.16": {"line.1": {"text": "Doch Helden schreckt nicht Rad noch Wippe,", "tokens": ["Doch", "Hel\u00b7den", "schreckt", "nicht", "Rad", "noch", "Wip\u00b7pe", ","], "token_info": ["word", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["KON", "NN", "VVFIN", "PTKNEG", "NN", "ADV", "NN", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Nicht Cerberus.", "tokens": ["Nicht", "Cer\u00b7be\u00b7rus", "."], "token_info": ["word", "word", "punct"], "pos": ["PTKNEG", "NE", "$."], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Pipin dringt vor und spitzt die Lippe,", "tokens": ["Pi\u00b7pin", "dringt", "vor", "und", "spitzt", "die", "Lip\u00b7pe", ","], "token_info": ["word", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["NE", "VVFIN", "PTKVZ", "KON", "VVFIN", "ART", "NN", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Und raubt den Ku\u00df.", "tokens": ["Und", "raubt", "den", "Ku\u00df", "."], "token_info": ["word", "word", "word", "word", "punct"], "pos": ["KON", "VVFIN", "ART", "NN", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.17": {"line.1": {"text": "O Greuel! Kaum ber\u00fchrt er Berthen", "tokens": ["O", "Greu\u00b7el", "!", "Kaum", "be\u00b7r\u00fchrt", "er", "Bert\u00b7hen"], "token_info": ["word", "word", "punct", "word", "word", "word", "word"], "pos": ["NE", "NE", "$.", "ADV", "VVFIN", "PPER", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Mit seinem Bart,", "tokens": ["Mit", "sei\u00b7nem", "Bart", ","], "token_info": ["word", "word", "word", "punct"], "pos": ["APPR", "PPOSAT", "NN", "$,"], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Als sie, statt ganz ein Weib zu werden,", "tokens": ["Als", "sie", ",", "statt", "ganz", "ein", "Weib", "zu", "wer\u00b7den", ","], "token_info": ["word", "word", "punct", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["KOUS", "PPER", "$,", "KOUI", "ADV", "ART", "NN", "PTKZU", "VAINF", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Ganz Drache ward.", "tokens": ["Ganz", "Dra\u00b7che", "ward", "."], "token_info": ["word", "word", "word", "punct"], "pos": ["ADV", "NN", "VAFIN", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.18": {"line.1": {"text": "Sie st\u00fcrzt ihm zischend in die Arme:", "tokens": ["Sie", "st\u00fcrzt", "ihm", "zi\u00b7schend", "in", "die", "Ar\u00b7me", ":"], "token_info": ["word", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["PPER", "VVFIN", "PPER", "VVPP", "APPR", "ART", "NN", "$."], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Er bebt zur\u00fcck;", "tokens": ["Er", "bebt", "zu\u00b7r\u00fcck", ";"], "token_info": ["word", "word", "word", "punct"], "pos": ["PPER", "VVFIN", "PTKVZ", "$."], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Doch knacks bricht sie, da\u00df Gott erbarme!", "tokens": ["Doch", "knacks", "bricht", "sie", ",", "da\u00df", "Gott", "er\u00b7bar\u00b7me", "!"], "token_info": ["word", "word", "word", "word", "punct", "word", "word", "word", "punct"], "pos": ["KON", "NN", "VVFIN", "PPER", "$,", "KOUS", "NN", "VVFIN", "$."], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Ihm das Genick.", "tokens": ["Ihm", "das", "Ge\u00b7nick", "."], "token_info": ["word", "word", "word", "punct"], "pos": ["PPER", "ART", "NN", "$."], "meter": "+--+", "measure": "iambic.di.chol"}}, "stanza.19": {"line.1": {"text": "Nun h\u00f6rt man ihn mit wildem Prasseln,", "tokens": ["Nun", "h\u00f6rt", "man", "ihn", "mit", "wil\u00b7dem", "Pras\u00b7seln", ","], "token_info": ["word", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["ADV", "VVFIN", "PIS", "PPER", "APPR", "ADJA", "NN", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Durch Thal und Hayn,", "tokens": ["Durch", "Thal", "und", "Hayn", ","], "token_info": ["word", "word", "word", "word", "punct"], "pos": ["APPR", "NN", "KON", "NN", "$,"], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Um Mitternacht im Gelde rasseln", "tokens": ["Um", "Mit\u00b7ter\u00b7nacht", "im", "Gel\u00b7de", "ras\u00b7seln"], "token_info": ["word", "word", "word", "word", "word"], "pos": ["KOUI", "NN", "APPRART", "NN", "VVINF"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Und Zetter schreyn.", "tokens": ["Und", "Zet\u00b7ter", "schreyn", "."], "token_info": ["word", "word", "word", "punct"], "pos": ["KON", "NN", "VAINF", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.20": {"line.1": {"text": "Wie mancher ruft bey diesem M\u00e4hrchen", "tokens": ["Wie", "man\u00b7cher", "ruft", "bey", "die\u00b7sem", "M\u00e4hr\u00b7chen"], "token_info": ["word", "word", "word", "word", "word", "word"], "pos": ["PWAV", "PIS", "VVFIN", "APPR", "PDAT", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Voll Herzeleid:", "tokens": ["Voll", "Her\u00b7ze\u00b7leid", ":"], "token_info": ["word", "word", "punct"], "pos": ["ADJD", "NN", "$."], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Ich Thor! auch ich hab ein Meg\u00e4rchen", "tokens": ["Ich", "Thor", "!", "auch", "ich", "hab", "ein", "Me\u00b7g\u00e4r\u00b7chen"], "token_info": ["word", "word", "punct", "word", "word", "word", "word", "word"], "pos": ["PPER", "NN", "$.", "ADV", "PPER", "VAFIN", "ART", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Ums Geld gefreyt.", "tokens": ["Ums", "Geld", "ge\u00b7freyt", "."], "token_info": ["word", "word", "word", "punct"], "pos": ["KOUI", "NN", "VVPP", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.21": {"line.1": {"text": "Auf einem Schlosse der Vogesen,", "tokens": ["Auf", "ei\u00b7nem", "Schlos\u00b7se", "der", "Vo\u00b7ge\u00b7sen", ","], "token_info": ["word", "word", "word", "word", "word", "punct"], "pos": ["APPR", "ART", "NN", "ART", "NN", "$,"], "meter": "-+-+--+--", "measure": "iambic.tri.relaxed"}, "line.2": {"text": "Das Blitzburg hei\u00dft,", "tokens": ["Das", "Blitz\u00b7burg", "hei\u00dft", ","], "token_info": ["word", "word", "word", "punct"], "pos": ["ART", "NN", "VVFIN", "$,"], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Spuckt, wie wir in der Chronick lesen,", "tokens": ["Spuckt", ",", "wie", "wir", "in", "der", "Chro\u00b7nick", "le\u00b7sen", ","], "token_info": ["word", "punct", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["VVFIN", "$,", "PWAV", "PPER", "APPR", "ART", "NN", "VVINF", "$,"], "meter": "-+-+--+--", "measure": "iambic.tri.relaxed"}, "line.4": {"text": "Ein Poltergeist.", "tokens": ["Ein", "Pol\u00b7ter\u00b7geist", "."], "token_info": ["word", "word", "punct"], "pos": ["ART", "NN", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.22": {"line.1": {"text": "Ein Fr\u00e4ulein ists, halb Weib halb Drache,", "tokens": ["Ein", "Fr\u00e4u\u00b7lein", "ists", ",", "halb", "Weib", "halb", "Dra\u00b7che", ","], "token_info": ["word", "word", "word", "punct", "word", "word", "word", "word", "punct"], "pos": ["ART", "NN", "VAFIN", "$,", "ADJD", "NN", "ADJD", "NN", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Aus Schwabenland,", "tokens": ["Aus", "Schwa\u00b7ben\u00b7land", ","], "token_info": ["word", "word", "punct"], "pos": ["APPR", "NN", "$,"], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Von einer b\u00f6sen Fey aus Rache", "tokens": ["Von", "ei\u00b7ner", "b\u00f6\u00b7sen", "Fey", "aus", "Ra\u00b7che"], "token_info": ["word", "word", "word", "word", "word", "word"], "pos": ["APPR", "ART", "ADJA", "NN", "APPR", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Hieher gebannt.", "tokens": ["Hie\u00b7her", "ge\u00b7bannt", "."], "token_info": ["word", "word", "punct"], "pos": ["ADV", "VVPP", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.23": {"line.1": {"text": "Es liegen hunderttausend Kronen", "tokens": ["Es", "lie\u00b7gen", "hun\u00b7dert\u00b7tau\u00b7send", "Kro\u00b7nen"], "token_info": ["word", "word", "word", "word"], "pos": ["PPER", "VVFIN", "CARD", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Bereit, den Held,", "tokens": ["Be\u00b7reit", ",", "den", "Held", ","], "token_info": ["word", "punct", "word", "word", "punct"], "pos": ["NN", "$,", "ART", "NN", "$,"], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Der sie erl\u00f6set, zu belohnen.", "tokens": ["Der", "sie", "er\u00b7l\u00f6\u00b7set", ",", "zu", "be\u00b7loh\u00b7nen", "."], "token_info": ["word", "word", "word", "punct", "word", "word", "punct"], "pos": ["ART", "PPER", "VVFIN", "$,", "PTKZU", "VVINF", "$."], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Ein sch\u00f6nes Geld!", "tokens": ["Ein", "sch\u00f6\u00b7nes", "Geld", "!"], "token_info": ["word", "word", "word", "punct"], "pos": ["ART", "ADJA", "NN", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.24": {"line.1": {"text": "Zehn Ritter hatten sich verb\u00fcrget,", "tokens": ["Zehn", "Rit\u00b7ter", "hat\u00b7ten", "sich", "ver\u00b7b\u00fcr\u00b7get", ","], "token_info": ["word", "word", "word", "word", "word", "punct"], "pos": ["CARD", "NN", "VAFIN", "PRF", "VVFIN", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Sie zu befreyn;", "tokens": ["Sie", "zu", "be\u00b7freyn", ";"], "token_info": ["word", "word", "word", "punct"], "pos": ["PPER", "PTKZU", "VVINF", "$."], "meter": "+--+", "measure": "iambic.di.chol"}, "line.3": {"text": "Allein man fand sie all erw\u00fcrget", "tokens": ["Al\u00b7lein", "man", "fand", "sie", "all", "er\u00b7w\u00fcr\u00b7get"], "token_info": ["word", "word", "word", "word", "word", "word"], "pos": ["ADV", "PIS", "VVFIN", "PPER", "PIAT", "VVFIN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Im nahen Hayn.", "tokens": ["Im", "na\u00b7hen", "Hayn", "."], "token_info": ["word", "word", "word", "punct"], "pos": ["APPRART", "ADJA", "NN", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.25": {"line.1": {"text": "Pipin, ein Knapp aus Lotharingen,", "tokens": ["Pi\u00b7pin", ",", "ein", "Knapp", "aus", "Lo\u00b7tha\u00b7rin\u00b7gen", ","], "token_info": ["word", "punct", "word", "word", "word", "word", "punct"], "pos": ["NE", "$,", "ART", "NN", "APPR", "NN", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Der Kempen Schmuck,", "tokens": ["Der", "Kem\u00b7pen", "Schmuck", ","], "token_info": ["word", "word", "word", "punct"], "pos": ["ART", "NN", "NN", "$,"], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Von dem noch heut die Ammen singen,", "tokens": ["Von", "dem", "noch", "heut", "die", "Am\u00b7men", "sin\u00b7gen", ","], "token_info": ["word", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["APPR", "ART", "ADV", "ADV", "ART", "NN", "VVINF", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Vernahm den Spuck.", "tokens": ["Ver\u00b7nahm", "den", "Spuck", "."], "token_info": ["word", "word", "word", "punct"], "pos": ["VVFIN", "ART", "NN", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.26": {"line.1": {"text": "Nun kann er weder ruhn noch rasten;", "tokens": ["Nun", "kann", "er", "we\u00b7der", "ruhn", "noch", "ras\u00b7ten", ";"], "token_info": ["word", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["ADV", "VMFIN", "PPER", "KON", "VVFIN", "ADV", "VVFIN", "$."], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Er steigt zu Ro\u00df,", "tokens": ["Er", "steigt", "zu", "Ro\u00df", ","], "token_info": ["word", "word", "word", "word", "punct"], "pos": ["PPER", "VVFIN", "APPR", "NN", "$,"], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Und k\u00f6mmt am Abend vor Frohnfasten", "tokens": ["Und", "k\u00f6mmt", "am", "A\u00b7bend", "vor", "Frohn\u00b7fas\u00b7ten"], "token_info": ["word", "word", "word", "word", "word", "word"], "pos": ["KON", "VVFIN", "APPRART", "NN", "APPR", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Zum Zauberschlo\u00df.", "tokens": ["Zum", "Zau\u00b7ber\u00b7schlo\u00df", "."], "token_info": ["word", "word", "punct"], "pos": ["APPRART", "NN", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.27": {"line.1": {"text": "Er setzt, ermattet von der Reise,", "tokens": ["Er", "setzt", ",", "er\u00b7mat\u00b7tet", "von", "der", "Rei\u00b7se", ","], "token_info": ["word", "word", "punct", "word", "word", "word", "word", "punct"], "pos": ["PPER", "VVFIN", "$,", "VVFIN", "APPR", "ART", "NN", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Am Thor sich hin:", "tokens": ["Am", "Thor", "sich", "hin", ":"], "token_info": ["word", "word", "word", "word", "punct"], "pos": ["APPRART", "NN", "PRF", "PTKVZ", "$."], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Drey Uhus und drey Flederm\u00e4use", "tokens": ["Drey", "U\u00b7hus", "und", "drey", "Fle\u00b7der\u00b7m\u00e4u\u00b7se"], "token_info": ["word", "word", "word", "word", "word"], "pos": ["CARD", "NN", "KON", "CARD", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Begr\u00fc\u00dfen ihn.", "tokens": ["Be\u00b7gr\u00fc\u00b7\u00dfen", "ihn", "."], "token_info": ["word", "word", "punct"], "pos": ["VVFIN", "PPER", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.28": {"line.1": {"text": "Kaum schlug es zw\u00f6lf in der Kapelle,", "tokens": ["Kaum", "schlug", "es", "zw\u00f6lf", "in", "der", "Ka\u00b7pel\u00b7le", ","], "token_info": ["word", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["ADV", "VVFIN", "PPER", "CARD", "APPR", "ART", "NN", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "So barst das Thor:", "tokens": ["So", "barst", "das", "Thor", ":"], "token_info": ["word", "word", "word", "word", "punct"], "pos": ["ADV", "ADV", "ART", "NN", "$."], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Ein gro\u00dfer Hund lag vor der Schwelle,", "tokens": ["Ein", "gro\u00b7\u00dfer", "Hund", "lag", "vor", "der", "Schwel\u00b7le", ","], "token_info": ["word", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["ART", "ADJA", "NN", "VVFIN", "APPR", "ART", "NN", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Schwarz wie ein Mohr.", "tokens": ["Schwarz", "wie", "ein", "Mohr", "."], "token_info": ["word", "word", "word", "word", "punct"], "pos": ["NE", "KOKOM", "ART", "NN", "$."], "meter": "+--+", "measure": "iambic.di.chol"}}, "stanza.29": {"line.1": {"text": "Was thut der Knapp? Er macht in Eile", "tokens": ["Was", "thut", "der", "Knapp", "?", "Er", "macht", "in", "Ei\u00b7le"], "token_info": ["word", "word", "word", "word", "punct", "word", "word", "word", "word"], "pos": ["PWS", "VVFIN", "ART", "NN", "$.", "PPER", "VVFIN", "APPR", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Das Kreutz auf ihn;", "tokens": ["Das", "Kreutz", "auf", "ihn", ";"], "token_info": ["word", "word", "word", "word", "punct"], "pos": ["ART", "NN", "APPR", "PPER", "$."], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Husch! flieht das Thier mit Angstgeheule,", "tokens": ["Husch", "!", "flieht", "das", "Thier", "mit", "Angst\u00b7ge\u00b7heu\u00b7le", ","], "token_info": ["word", "punct", "word", "word", "word", "word", "word", "punct"], "pos": ["NE", "$.", "VVFIN", "ART", "NN", "APPR", "NN", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Gott wei\u00df, wohin?", "tokens": ["Gott", "wei\u00df", ",", "wo\u00b7hin", "?"], "token_info": ["word", "word", "punct", "word", "punct"], "pos": ["NN", "VVFIN", "$,", "PWAV", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.30": {"line.1": {"text": "Nun f\u00fchrt ein Irrwisch ihn zum Throne,", "tokens": ["Nun", "f\u00fchrt", "ein", "Irr\u00b7wisch", "ihn", "zum", "Thro\u00b7ne", ","], "token_info": ["word", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["ADV", "VVFIN", "ART", "NN", "PPER", "APPRART", "NN", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Wo Bertha sa\u00df,", "tokens": ["Wo", "Bert\u00b7ha", "sa\u00df", ","], "token_info": ["word", "word", "word", "punct"], "pos": ["PWAV", "NE", "VVFIN", "$,"], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Bekr\u00e4nzt mit einer Grafenkrone", "tokens": ["Be\u00b7kr\u00e4nzt", "mit", "ei\u00b7ner", "Gra\u00b7fen\u00b7kro\u00b7ne"], "token_info": ["word", "word", "word", "word"], "pos": ["VVPP", "APPR", "ART", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Von Chrysopras.", "tokens": ["Von", "Chry\u00b7so\u00b7pras", "."], "token_info": ["word", "word", "punct"], "pos": ["APPR", "NE", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.31": {"line.1": {"text": "Bla\u00df, wie der Nix, erschien die Dirne:", "tokens": ["Bla\u00df", ",", "wie", "der", "Nix", ",", "er\u00b7schien", "die", "Dir\u00b7ne", ":"], "token_info": ["word", "punct", "word", "word", "word", "punct", "word", "word", "word", "punct"], "pos": ["NN", "$,", "PWAV", "ART", "NE", "$,", "VVFIN", "ART", "NN", "$."], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Ihr Augenpaar", "tokens": ["Ihr", "Au\u00b7gen\u00b7paar"], "token_info": ["word", "word"], "pos": ["PPOSAT", "NN"], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "War gr\u00fcn, ein Vorgeb\u00fcrg die Stirne,", "tokens": ["War", "gr\u00fcn", ",", "ein", "Vor\u00b7ge\u00b7b\u00fcrg", "die", "Stir\u00b7ne", ","], "token_info": ["word", "word", "punct", "word", "word", "word", "word", "punct"], "pos": ["VAFIN", "ADJD", "$,", "ART", "NN", "ART", "NN", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Und fahl ihr Haar.", "tokens": ["Und", "fahl", "ihr", "Haar", "."], "token_info": ["word", "word", "word", "word", "punct"], "pos": ["KON", "VVFIN", "PPOSAT", "NN", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.32": {"line.1": {"text": "Der Junker stutzt, blickt nach dem Schatze,", "tokens": ["Der", "Jun\u00b7ker", "stutzt", ",", "blickt", "nach", "dem", "Schat\u00b7ze", ","], "token_info": ["word", "word", "word", "punct", "word", "word", "word", "word", "punct"], "pos": ["ART", "NN", "VVFIN", "$,", "VVFIN", "APPR", "ART", "NN", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Und bey dem Glanz", "tokens": ["Und", "bey", "dem", "Glanz"], "token_info": ["word", "word", "word", "word"], "pos": ["KON", "APPR", "ART", "NN"], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Des Golds, vergi\u00dft er Berthens Fratze", "tokens": ["Des", "Golds", ",", "ver\u00b7gi\u00dft", "er", "Bert\u00b7hens", "Frat\u00b7ze"], "token_info": ["word", "word", "punct", "word", "word", "word", "word"], "pos": ["ART", "NN", "$,", "VVFIN", "PPER", "NE", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Und Schuppenschwanz.", "tokens": ["Und", "Schup\u00b7pen\u00b7schwanz", "."], "token_info": ["word", "word", "punct"], "pos": ["KON", "NN", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.33": {"line.1": {"text": "Wie soll ich dir die Freyheit geben,", "tokens": ["Wie", "soll", "ich", "dir", "die", "Frey\u00b7heit", "ge\u00b7ben", ","], "token_info": ["word", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["PWAV", "VMFIN", "PPER", "PPER", "ART", "NN", "VVINF", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Du holde Magd?", "tokens": ["Du", "hol\u00b7de", "Magd", "?"], "token_info": ["word", "word", "word", "punct"], "pos": ["PPER", "ADJA", "NN", "$."], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Sprach er: Mit Freuden sey mein Leben", "tokens": ["Sprach", "er", ":", "Mit", "Freu\u00b7den", "sey", "mein", "Le\u00b7ben"], "token_info": ["word", "word", "punct", "word", "word", "word", "word", "word"], "pos": ["NN", "PPER", "$.", "APPR", "NN", "VAFIN", "PPOSAT", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Daran gewagt!", "tokens": ["Da\u00b7ran", "ge\u00b7wagt", "!"], "token_info": ["word", "word", "punct"], "pos": ["PAV", "VVPP", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.34": {"line.1": {"text": "Ein Ku\u00df erl\u00f6st mich, sprach die Holde", "tokens": ["Ein", "Ku\u00df", "er\u00b7l\u00f6st", "mich", ",", "sprach", "die", "Hol\u00b7de"], "token_info": ["word", "word", "word", "word", "punct", "word", "word", "word"], "pos": ["ART", "NN", "VVFIN", "PPER", "$,", "VVFIN", "ART", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Vom Schlangenleib,", "tokens": ["Vom", "Schlan\u00b7gen\u00b7leib", ","], "token_info": ["word", "word", "punct"], "pos": ["APPRART", "NN", "$,"], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Und schenkt mich dir mit diesem Golde", "tokens": ["Und", "schenkt", "mich", "dir", "mit", "die\u00b7sem", "Gol\u00b7de"], "token_info": ["word", "word", "word", "word", "word", "word", "word"], "pos": ["KON", "VVFIN", "PPER", "PPER", "APPR", "PDAT", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Zum Eheweib.", "tokens": ["Zum", "E\u00b7he\u00b7weib", "."], "token_info": ["word", "word", "punct"], "pos": ["APPRART", "NN", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.35": {"line.1": {"text": "Er naht sich ihr mit k\u00fchner Hitze;", "tokens": ["Er", "naht", "sich", "ihr", "mit", "k\u00fch\u00b7ner", "Hit\u00b7ze", ";"], "token_info": ["word", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["PPER", "VVFIN", "PRF", "PPER", "APPR", "ADJA", "NN", "$."], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Ha! welch ein Graus!", "tokens": ["Ha", "!", "welch", "ein", "Graus", "!"], "token_info": ["word", "punct", "word", "word", "word", "punct"], "pos": ["ITJ", "$.", "PWAT", "ART", "NN", "$."], "meter": "+--+", "measure": "iambic.di.chol"}, "line.3": {"text": "Ihr Mund speit pl\u00f6tzlich rothe Blitze", "tokens": ["Ihr", "Mund", "speit", "pl\u00f6tz\u00b7lich", "ro\u00b7the", "Blit\u00b7ze"], "token_info": ["word", "word", "word", "word", "word", "word"], "pos": ["PPOSAT", "NN", "VVFIN", "ADJD", "ADJA", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Und Donner aus.", "tokens": ["Und", "Don\u00b7ner", "aus", "."], "token_info": ["word", "word", "word", "punct"], "pos": ["KON", "NN", "PTKVZ", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.36": {"line.1": {"text": "Doch Helden schreckt nicht Rad noch Wippe,", "tokens": ["Doch", "Hel\u00b7den", "schreckt", "nicht", "Rad", "noch", "Wip\u00b7pe", ","], "token_info": ["word", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["KON", "NN", "VVFIN", "PTKNEG", "NN", "ADV", "NN", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Nicht Cerberus.", "tokens": ["Nicht", "Cer\u00b7be\u00b7rus", "."], "token_info": ["word", "word", "punct"], "pos": ["PTKNEG", "NE", "$."], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Pipin dringt vor und spitzt die Lippe,", "tokens": ["Pi\u00b7pin", "dringt", "vor", "und", "spitzt", "die", "Lip\u00b7pe", ","], "token_info": ["word", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["NE", "VVFIN", "PTKVZ", "KON", "VVFIN", "ART", "NN", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Und raubt den Ku\u00df.", "tokens": ["Und", "raubt", "den", "Ku\u00df", "."], "token_info": ["word", "word", "word", "word", "punct"], "pos": ["KON", "VVFIN", "ART", "NN", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.37": {"line.1": {"text": "O Greuel! Kaum ber\u00fchrt er Berthen", "tokens": ["O", "Greu\u00b7el", "!", "Kaum", "be\u00b7r\u00fchrt", "er", "Bert\u00b7hen"], "token_info": ["word", "word", "punct", "word", "word", "word", "word"], "pos": ["NE", "NE", "$.", "ADV", "VVFIN", "PPER", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Mit seinem Bart,", "tokens": ["Mit", "sei\u00b7nem", "Bart", ","], "token_info": ["word", "word", "word", "punct"], "pos": ["APPR", "PPOSAT", "NN", "$,"], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Als sie, statt ganz ein Weib zu werden,", "tokens": ["Als", "sie", ",", "statt", "ganz", "ein", "Weib", "zu", "wer\u00b7den", ","], "token_info": ["word", "word", "punct", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["KOUS", "PPER", "$,", "KOUI", "ADV", "ART", "NN", "PTKZU", "VAINF", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Ganz Drache ward.", "tokens": ["Ganz", "Dra\u00b7che", "ward", "."], "token_info": ["word", "word", "word", "punct"], "pos": ["ADV", "NN", "VAFIN", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.38": {"line.1": {"text": "Sie st\u00fcrzt ihm zischend in die Arme:", "tokens": ["Sie", "st\u00fcrzt", "ihm", "zi\u00b7schend", "in", "die", "Ar\u00b7me", ":"], "token_info": ["word", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["PPER", "VVFIN", "PPER", "VVPP", "APPR", "ART", "NN", "$."], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Er bebt zur\u00fcck;", "tokens": ["Er", "bebt", "zu\u00b7r\u00fcck", ";"], "token_info": ["word", "word", "word", "punct"], "pos": ["PPER", "VVFIN", "PTKVZ", "$."], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Doch knacks bricht sie, da\u00df Gott erbarme!", "tokens": ["Doch", "knacks", "bricht", "sie", ",", "da\u00df", "Gott", "er\u00b7bar\u00b7me", "!"], "token_info": ["word", "word", "word", "word", "punct", "word", "word", "word", "punct"], "pos": ["KON", "NN", "VVFIN", "PPER", "$,", "KOUS", "NN", "VVFIN", "$."], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Ihm das Genick.", "tokens": ["Ihm", "das", "Ge\u00b7nick", "."], "token_info": ["word", "word", "word", "punct"], "pos": ["PPER", "ART", "NN", "$."], "meter": "+--+", "measure": "iambic.di.chol"}}, "stanza.39": {"line.1": {"text": "Nun h\u00f6rt man ihn mit wildem Prasseln,", "tokens": ["Nun", "h\u00f6rt", "man", "ihn", "mit", "wil\u00b7dem", "Pras\u00b7seln", ","], "token_info": ["word", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["ADV", "VVFIN", "PIS", "PPER", "APPR", "ADJA", "NN", "$,"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Durch Thal und Hayn,", "tokens": ["Durch", "Thal", "und", "Hayn", ","], "token_info": ["word", "word", "word", "word", "punct"], "pos": ["APPR", "NN", "KON", "NN", "$,"], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Um Mitternacht im Gelde rasseln", "tokens": ["Um", "Mit\u00b7ter\u00b7nacht", "im", "Gel\u00b7de", "ras\u00b7seln"], "token_info": ["word", "word", "word", "word", "word"], "pos": ["KOUI", "NN", "APPRART", "NN", "VVINF"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Und Zetter schreyn.", "tokens": ["Und", "Zet\u00b7ter", "schreyn", "."], "token_info": ["word", "word", "word", "punct"], "pos": ["KON", "NN", "VAINF", "$."], "meter": "-+-+", "measure": "iambic.di"}}, "stanza.40": {"line.1": {"text": "Wie mancher ruft bey diesem M\u00e4hrchen", "tokens": ["Wie", "man\u00b7cher", "ruft", "bey", "die\u00b7sem", "M\u00e4hr\u00b7chen"], "token_info": ["word", "word", "word", "word", "word", "word"], "pos": ["PWAV", "PIS", "VVFIN", "APPR", "PDAT", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.2": {"text": "Voll Herzeleid:", "tokens": ["Voll", "Her\u00b7ze\u00b7leid", ":"], "token_info": ["word", "word", "punct"], "pos": ["ADJD", "NN", "$."], "meter": "-+-+", "measure": "iambic.di"}, "line.3": {"text": "Ich Thor! auch ich hab ein Meg\u00e4rchen", "tokens": ["Ich", "Thor", "!", "auch", "ich", "hab", "ein", "Me\u00b7g\u00e4r\u00b7chen"], "token_info": ["word", "word", "punct", "word", "word", "word", "word", "word"], "pos": ["PPER", "NN", "$.", "ADV", "PPER", "VAFIN", "ART", "NN"], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.4": {"text": "Ums Geld gefreyt.", "tokens": ["Ums", "Geld", "ge\u00b7freyt", "."], "token_info": ["word", "word", "word", "punct"], "pos": ["KOUI", "NN", "VVPP", "$."], "meter": "-+-+", "measure": "iambic.di"}}}}} |
{"word": "stonewall", "accent": "/\u02c8sto\u028anw\u0254\u02d0l/", "mean_cn": "v. \u963b\u788d\u8bae\u4e8b\uff0c\u59a8\u788d", "mean_en": "to refuse or fail to answer questions, to do what has been requested, etc., especially in order to delay or prevent something", "sentence": "She'll never agree - she always stonewalls me whenever I ask her.", "sentence_trans": "\u5979\u4e0d\u4f1a\u540c\u610f\u7684\uff0c\u56e0\u4e3a\u6bcf\u6b21\u5979\u90fd\u6bc5\u7136\u51b3\u7136\u5730\u62d2\u7edd\u6211\u3002", "sentence_phrase": "", "word_etyma": "stone\u77f3\u5934 \uff0b wall\u5899\u58c1 \u2192 \u77f3\u5934\u5899\u58c1 \u2192 stonewall\u59a8\u788d", "cloze_data": {"syllable": "stonewall", "cloze": "stonew[all]", "options": ["ald|alk|alm|alt|ial"], "tips": [[]]}} |
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{"dta.poem.20825": {"metadata": {"author": {"name": "Hofmannswaldau, Christian Hofmann von", "birth": "N.A.", "death": "N.A."}, "title": "Der \n Sterbende \n Socrates.", "genre": "Lyrik; Prosa; Drama", "period": "N.A.", "pub_year": "1679", "urn": "urn:nbn:de:kobv:b4-20289-1", "language": ["de:0.71", "en:0.14", "da:0.14"], "booktitle": "Hofmann von Hofmannswaldau, Christian: Deutsche Ubersetzungen und Gedichte. Breslau, 1679."}, "poem": {"stanza.1": {"line.1": {"text": "Und rath ihm/ wo er folgen kan/", "tokens": ["Und", "rath", "ihm", "/", "wo", "er", "fol\u00b7gen", "kan", "/"], "token_info": ["word", "word", "word", "punct", "word", "word", "word", "word", "punct"], "pos": ["KON", "NN", "PPER", "$(", "PWAV", "PPER", "VVINF", "VMFIN", "$("], "meter": "-+-+-+-+", "measure": "iambic.tetra"}, "line.2": {"text": "Sich mit mir aus der Welt zu wenden/", "tokens": ["Sich", "mit", "mir", "aus", "der", "Welt", "zu", "wen\u00b7den", "/"], "token_info": ["word", "word", "word", "word", "word", "word", "word", "word", "punct"], "pos": ["PRF", "APPR", "PPER", "APPR", "ART", "NN", "PTKZU", "VVINF", "$("], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}, "line.3": {"text": "Die Stunde r\u00fcckt nunmehr heran/", "tokens": ["Die", "Stun\u00b7de", "r\u00fcckt", "nun\u00b7mehr", "he\u00b7ran", "/"], "token_info": ["word", "word", "word", "word", "word", "punct"], "pos": ["ART", "NN", "VVFIN", "ADV", "PTKVZ", "$("], "meter": "-+-+-+-+", "measure": "iambic.tetra"}, "line.4": {"text": "Da\u00df ich mein Leben sol vollenden.", "tokens": ["Da\u00df", "ich", "mein", "Le\u00b7ben", "sol", "voll\u00b7en\u00b7den", "."], "token_info": ["word", "word", "word", "word", "word", "word", "punct"], "pos": ["KOUS", "PPER", "PPOSAT", "NN", "VMFIN", "VVINF", "$."], "meter": "-+-+-+-+-", "measure": "iambic.tetra"}}}}} |
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{
"url": "https://en.wikipedia.org/wiki/Acacia_acanthoclada",
"classification": [
{
"Kingdom": "Plantae"
},
{
"Clade": "Tracheophytes"
},
{
"Clade": "Angiosperms"
},
{
"Clade": "Eudicots"
},
{
"Clade": "Rosids"
},
{
"Order": "Fabales"
},
{
"Family": "Fabaceae"
},
{
"Clade": "Mimosoideae"
},
{
"Genus": "Acacia"
},
{
"Species": "A. acanthoclada"
}
],
"text": {
"__SUMMARY__": "Acacia acanthoclada, commonly known as harrow wattle, is a low, divaricate, highly branched and spinescent shrub that is endemic to Australia.",
"Description": "It grows up to 2 metres high and has phyllodes which measure 0.2 to 0.6 cm long and 1 to 2 mm wide. The phyllodes are straight, narrow-cuneate, slightly notched at the apex, and feature prominent midveins. Branchlets are terete, whitish and densely pubescent, As the branch grows it becomes glabrous and terminates in a rigid spinose point. The bark is grey, white or occasionally greenish.\nThe golden-yellow flowerheads, on 5–15 cm long peduncles, appear at the phyllode axils. Flower parts are pentamerous, with the sepals fused into a synsepalous calyx. Flowers appear from August to October, followed by irregularly twisted, glaucous, brown seed pods which are 3 to 6 cm long and 3 to 6 mm wide.\nIts occurs naturally in Western Australia, South Australia and Victoria and is listed as endangered under the Threatened Species Conservation Act in New South Wales.",
"Taxonomy": "The species was formally described in 1863 by Victorian Government Botanist Ferdinand von Mueller in the third volume of his Fragmenta Phytographiae Australiae, based on plant material collected near Kulkyne, Victoria.Two subspecies are recognised:\n\nA. acanthoclada F.Muell. subsp. acanthoclada\nA. acanthoclada subsp. glaucescens Maslin",
"Distribution": "The species is relatively uncommon and is found scattered at several sites in isolated clumps: Buronga, Wentworth and Pooncarie districts in far south-western New South Wales, Victoria, South Australia and Arumpo Station and Montarna Station in Western Australia. Arumpo Station in particular is home to a stunted sample, which is threatened by overgrazing by kangaroos.",
"Habitat": "This species usually grows on deep, loose, sandy soil. It inhabits undisturbed mallee areas, often on ridges and dunes, and more rarely on rock outcrops.",
"See also": "List of Acacia speciesAssociated species:\n\nEucalyptus dumosa\nEucalyptus socialis\nEucalyptus gracilis\nEucalyptus costata\nCallitris verrucosa\nCodonocarpus cotinifolius\nTriodia scariosa",
"References": "",
"External links": "\"Acacia acanthoclada\". FloraBase. Western Australian Government Department of Parks and Wildlife. \nAustralian National Botanic Gardens: Photo of Acacia acanthoclada"
}
} |
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{
"answers": [
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]
],
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],
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{
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