2018年利物浦约翰摩尔大学ISC
2018年度国外数学竞赛试题翻译汇编
(升级版)
赵力 2019 - 06 - 19
时间，就像高铁，一眨眼，就过站了……
人生很简单 总有一些风景，注定要错过 与其执着，不如随缘 只要懂得“珍惜、知足、感恩”就可以了！
笑看世事繁华，淡定人生心态 不索不可取，不求不可得 学会感恩，做人做事，无憾我心 不再奢望浮华之梦，不再……
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2018 年亚太地区数学奥林匹克试题
时间，一点不像高铁，过了站，居然买不到回来的车票！
生命，不就如一场雨吗 你曾无知地在其间雀跃，曾痴迷地在其间沉吟 但更多时候 你得忍受那些寒冷与潮湿，那些无奈与寂寞 并且以晴日的幻想来度日
当你握紧双手，里面什么也没有 当你打开双手，世界就在你手中
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目录
2018 年亚太地区数学奥林匹克 ……………………………………… 1 2018 年波罗的海地区数学奥林匹克 ………………………………… 2 2018 年第 10 届 Benelux 数学奥林匹克 ……………………………… 5 2018 年巴尔干地区数学奥林匹克 …………………………………… 6 2018 年巴尔干地区数学奥林匹克预选题…………………………… 7 2018 年巴尔干地区初中数学奥林匹克 ……………………………… 10 2018 年高加索地区数学奥林匹克 …………………………………… 11 2018 年中美洲及加勒比地区数学奥林匹克 ………………………… 13 2018 年 Cono Sur 数学奥林匹克 ……………………………………… 14 2018 年捷克-波兰-斯洛伐克联合数学竞赛 ………………………… 15 2018 年捷克和斯洛伐克数学奥林匹克 ……………………………… 16 2018 年多瑙河地区数学奥林匹克 …………………………………… 17 2018 年欧洲女子数学奥林匹克 ……………………………………… 19 2018 年欧洲数学杯奥林匹克 ………………………………………… 21 2018 年拉丁美洲数学奥林匹克 ……………………………………… 23 2018 年国际大都市数学竞赛(IOM) ………………………………… 24 2018 年第 2 届 IMO 复仇赛 …………………………………………… 25 2018 年第 5 届伊朗几何奥林匹克 …………………………………… 26 2018 年第 17 届基辅数学节竞赛 …………………………………… 30 2018 年地中海地区数学竞赛 ………………………………………… 32 2018 年中欧数学奥林匹克 …………………………………………… 33 2018 年北欧数学奥林匹克 …………………………………………… 35 2018 年泛非数学奥林匹克 …………………………………………… 36 2018 年泛非数学奥林匹克预选题 …………………………………… 38 2018 年罗马尼亚大师杯数学奥林匹克 ……………………………… 42
基于模糊神经网络的球队评估系统
第 ４ 第ｌ期 ２卷 ２
文章 编 号 ：０ ６—９ ４ （０ ７ １ １０ ３ ８ ２ ０ ） ２—０ ６ １３—０ ３
计 算 机 仿 真
２ 年１月 ．７ ２ ０ ０
基 于 模 糊 神 经 网 络 的球 队评 估 系统
’
夏 汉 民
ｌｖ １ｏ ａ ｎ ｏ ｃ ｓ ｔ ｅ ｒ ｓ ｌ ｏ ｔｈ ｅ ｅ ｆ ｅ ｍｓａ ｄ ｆ ｒ ａ ｔ ｈ ｅ ｕ ｔ ｆ ｔ ｍａｃ ．Ｔｈ ｅ ｕ ｔｉ ｃ ｒｅ ｔａ ｄ ｖ ｌ ． ｅ ｒ ｓ ｌ Ｓ ｏ ｒ ｃ ｎ ａ ｉ ｄ
ＫＥＹＷ ＯＲＤＳ： ｕ ｚ ｏ ｉ ； ｅ ｒ ｌｎ ｔ ｏ ｋ Ｆ ｚ ｙ ｌ ｇ ｃ Ｎ ｕ ａ ｅ ｗ ｒ ；Ｔ ａ ｅ ａｕ ｔ ｎ ｓ ｓｅ ｅ ｍ ｖ ｌ ａｉ ｙ ｔ ｍ；Ｂ ｒｔ ｍｅ ｉ ｏ Ｐ ａｉ ｈ ｔｃ
１ 序 言
一
２ 模 糊球 队评 估模 型
球 队评估 系统 的输入 、 出之 间存 在 着某 些不 确定 因 输 素， 它们之 间的关系 ， 以通过模糊数学模 型加以描述 。 可 在球
个球 队在 比赛中的表现 由很 多 因素决 定 ， 且每一个 并
因素所起 的作用 并不一样 ， 如何根据不 同的 因素对 球队作 出
惯的逻辑含义 ， 适合用 于直 接 的或高层 的知 识表示 ， 较难 但 用它亲表示时变知识和过程 ， 而神经 网络则能够通 过学 习功
评估 的量化 集合。 有 ｍ种 不同的评估 因素 ， 设 则各种 不同的
当的改进。 实验证 明 ， 改进方法能够对球队的综合水平进行评估 和比赛结果 的预测 ， 结果准确有效 。
关键词 ： 模糊逻辑 ； 网络 ； 神经 球队评估系统
2018Times英国大学排名
排名学校中文名称学校英文名称1剑桥大学University of Cambridge2牛津大学University of Oxford3圣安德鲁斯大学University of St Andrews4帝国理工学院Imperial College London5杜伦大学Durham University6兰卡斯特大学Lancaster University7伦敦大学学院University College London7拉夫堡大学Loughborough University9华威大学The University of Warwick10利兹大学University of Leeds11伦敦政治经济学院The London School of Economics and Political Science12巴斯大学University of Bath13东英吉利大学University of East Anglia14埃克塞特大学University of Exeter15伯明翰大学University of Birmingham16布里斯托大学University of Bristol16约克大学The University of York18诺丁汉大学The University of Nottingham19萨里大学University of Surrey20格拉斯哥大学University of Glasgow21谢菲尔德大学The University of Sheffield22埃塞克斯大学University of Essex23邓迪大学University of Dundee24爱丁堡大学The University of Edinburgh25曼彻斯特大学The University of Manchester26纽卡斯尔大学Newcastle University27萨塞克斯大学University of Sussex28伦敦国王学院King's College London28伦敦大学皇家霍洛威学院Royal Holloway University of London 30南安普顿大学University of Southampton31肯特大学University of Kent32雷丁大学University of Reading33哈珀亚当斯大学学院Harper Adams University34莱斯特大学University of Leicester35卡迪夫大学Cardiff University36伦敦大学亚非学院SOAS University of London36斯旺西大学Swansea University38英国女王大学Queen's University Belfast39赫瑞·瓦特大学Heriot Watt University40阿伯丁大学University of Aberdeen41思克莱德大学University of Strathclyde42利物浦大学The University of Liverpool43伦敦大学玛丽女王学院Queen Mary University of London44考文垂大学Coventry University2018年TIMES英国大学综合排名45斯特林大学University of Stirling46阿斯顿大学Aston University47亚伯大学Aberystwyth University47白金汉大学The University of Buckingham47诺丁汉特伦特大学Nottingham Trent University50基尔大学Keele University51伯恩茅斯艺术大学Arts University Bournemouth52利物浦霍普大学Liverpool Hope University53朴茨茅斯大学University of Portsmouth54林肯大学University of Lincoln55班戈大学Bangor University56泰晤士河谷大学Thames Valley University57西英格兰大学University of the West of England 58创意艺术大学University for the Creative Arts59布鲁内尔大学Brunel University60诺里奇艺术大学Norwich University of the Arts61切斯特大学University of Chester61知山大学Edge Hill University63伦敦大学金史密斯学院Goldsmiths University of London 63斯坦福德郡大学Staffordshire University65哈德斯菲尔德大学University of Huddersfield66诺森比亚大学Northumbria University67德蒙福特大学De Montfort University67利兹三一大学Leeds Trinity University69法尔茅斯大学Falmouth University70利物浦约翰摩尔斯大学Liverpool John Moores University 70谢菲尔德哈勒姆大学Sheffield Hallam University72奇切斯特大学University of Chichester73阿尔斯特大学University of Ulster74罗汉普顿大学Roehampton University75布拉德福德大学University of Bradford75伦敦城市大学City University London75赫尔大学The University of Hull75圣乔治医学院St George's, University of London 79伯恩茅斯大学Bournemouth University80曼彻斯特城市大学Manchester Metropolitan University 81德比大学University of Derby82罗伯特戈登大学The Robert Gordon University83格鲁斯特郡大学University of Gloucestershire84牛津布鲁克斯大学Oxford Brookes University85温切斯特大学University of Winchester86普利茅斯大学University of Plymouth86英国皇家农学院Royal Agricultural University88索尔福德大学University of Salford89赫特福德大学University of Hertfordshire90卡迪夫都市大学Cardiff Metropolitan University91密德萨斯大学Middlesex University92提赛德大学Teesside University93中央兰开夏大学University of Central Lancashire94巴斯泉大学Bath Spa University95格罗斯泰特主教大学Bishop Grosseteste University96桑德兰大学University of Sunderland97北安普顿大学The University of Northampton98阿伯泰邓迪大学University of Abertay Dundee99特威克南圣玛丽大学St Mary's, Twickenham100南安普顿索伦特大学Southampton Solent University100西苏格兰大学University of the West of Scotland 102玛格丽特女王大学Queen Margaret University102伍斯特大学University of Worcester104圣大卫三一学院University of Wales Trinity Saint David 105伯明翰城市大学Birmingham City University106伦敦南岸大学London South Bank University106威斯敏斯特大学University of Westminster108贝德福德大学University of Bedfordshire109格拉斯哥喀里多尼亚大学Glasgow Caledonian University109格林威治大学The University of Greenwich111坎特伯雷大学Canterbury Christ Church University 112布莱顿大学University of Brighton113安格利亚鲁斯金大学Anglia Ruskin University114东伦敦大学University of East London115伦敦艺术大学University of the Arts London116龙比亚大学Napier University117金斯顿大学Kingston University118约克圣约翰大学York St John University119圣马克与圣约翰大学Plymouth Marjon University119南威尔士大学The University of South Wales121纽曼大学Newman University122伦敦大学伯克贝克学院Birkbeck, University Of London123利兹贝克特大学Leeds Beckett University124波尔顿大学University of Bolton125哥比亚大学University of Cumbria126新白金汉大学Buckinghamshire New University 127格林多大学Glyndwr University128伦敦都市大学London Metropolitan University129萨福克学院University Campus Suffolk。
利物浦约翰摩尔
利物浦约翰摩尔Liverpool JM语言要求：IELTS：6.0-6.5TOEFL (IBT)：90-91截止日期：15th January研究生入学标准：For Entry to Postgraduate Taught Programmes e.g. MA, MScHolders of a good bachelor degree (with a minimum average grade of 70% overall) from a recognised Chinese university will be considered for direct admission to postgraduate diploma or masters programmes.Students who possess a three year university diploma (DaZhuan) can also be considered on individual merit. As well as students with extensive work experience in their related area of study相关专业：International Public Health MScCourse contentAll students on this programme must take:International Health Development (30 credits) Shared Core modules:The MSc International Public Health programme shares a number of core modules with students on the other two programmes (Public Health and Public Health Intelligence).Thinking Critically about Public Health (15 credits)Integrated Research Methods (30 credits)Epidemiology (15 credits)Dissertation (60 credits)Option modulesOptions may be chosen from the list below. However, some modules offered are subject to availability; and student numbers as per university regulations.Violence (15 credits)Tobacco Control (15 credits)Qualitative Research (15 credits)Globalization & Public Health (15 credits)Health Protection (15 credits)Health Improvement (15 credits)Independent Specialist Study (15 creditsEntry RequirementsThe new programmes are open to a wide range of graduates or those with equivalent relevant experience.Standard entryA good honours degree, normally 2:2 or above in any related discipline. A good command of the English language is required for this programme. Normally an IELT score of 6.5 or above is accepted.Non standard entryFor those who do not have a good honours degree, the programme leader can take into account relevant professional qualifications (e.g. Nursing, Midwifery, Social Work, Youth and Community, Health Promotion, Environmental Health) and experience.Any participant who does not have a first degree must satisfy the programme team of their ability to study at master’s level through their demonstration of appropriate equivalent skills in the work place, for example, responsibility for report writing. The team may require evidence to be submitted, e.g. a portfolio of written and other work as part of the administration process. A good command of the English language is required for this programme. Normally an IELT score of 6.5 or above is acceptedHealth Sciences MResCourse contentThe programme entails 180 credits comprising core modules, optional modules and a research project. This structure conforms to the University's Modular Masters framework. The core modules (60 credits) comprise generic training in research methods, personal skills development and professional skills. The optional modules (60 credits) enable students to gain knowledge and technical competence in research skills and methodologies likely to be used for subsequent work.The MRes culminates in a research project (60 credits) which can, for example, serve as a pilot for a projected PhD or Professional Doctorate programme. The research project builds on literature search, project proposal, and feedback from technical oral presentations, carried out in the generic research training modules. The project will demonstrate advanced subject knowledge, including the command of specialist skills and research methods, to a standard, and of a volume, compatible with the research project component of taught Masters qualifications.Entry RequirementsNormally the minimum qualification for entry will be a good honours degree (i.e. a First Class or 2:1 Honours degree) or its equivalent in an appropriate subject. Experience in lieu of an honours degree may be taken into consideration.Health and Social Care Management MScCourse contentThis programme has been developed in response to the service improvement and modernisation agenda of the NHS. The policies of choice have made it essential for health and social care organisations to function within a competitive environment.It is multi-professional, flexible and innovative, enabling all health care professionals to develop their knowledge & skills in Health & Social care Management.The core modules include the following CPDs, each worth 20 credits:Improving service Delivery through Human Resource ManagementThe Economics of World Class CommissioningAdvancing Leadership For QualityStrategic Management & EntrepreneurshipOther modules include:Research Methods & Data Analysis (30 credits)Dissertation (60Credits)There is also a 10 credit option of either an individual study or Work based Learning module.Entry RequirementsA first degreeAppropriate work experience Non-graduate entry to the programme can be by virtue of a strong portfolio that meets the following criteria:Diploma in Higher Education & evidence of study equivalent to the standard of a First degree e.g Level 3 CPDs。
利物浦约翰摩尔大学
院校概况
利物浦约翰摩尔大学(JMU)始创于1823年，以约翰摩尔斯爵士命名。
有着培养高质量、高技能水平毕业生的悠久历史。
利物浦约翰摩尔大学位于利物浦市中心，开设200多个本科与硕士课程。
拥有24,000名学生和2，500多名教师，来自全世界80多个国家。
校长切丽布莱尔是现任英国首相布莱尔的夫人。
JMU的毕业生遍布于世界一流的公司如国际商用机器公司（IBM）、微软公司、马可尼公司、福特汽车公司，以及英国广播公司（BBC）等。
360教育集团说，利物浦约翰摩尔大学是一所教学与研究水平很高的大学,在英国教育委员会的教学质量评估(TQA)中获得“优秀”,由于课程设置的领先以及教学水平的优势，该校毕业生的就业率在英国名列前茅，94以上的毕业生在毕业后六个月之内找到合适的工作。
物理竞赛之国际物理奥林匹克竞赛举办地
国际物理奥林匹克竞赛举办地2022年泰国2021年印度尼西亚2020年立陶宛2019年以色列2018年葡萄牙2017年摩尔达维亚2016年瑞士和列支敦士登2015年都柏林，爱尔兰2014年哈萨克2013年哥本哈根丹麦2012年爱沙尼亚2011年泰国2010年萨格勒布, 克罗地亚2009年梅里达, 墨西哥2008年河内, 越南2007年伊斯法罕, 伊朗2006年新加坡2005年萨拉曼卡, 西班牙2004年浦项, 韩国2003年台北, 台湾2002年巴厘岛, 印度尼西亚2001年安塔利亚, 土耳其2000年莱斯特, 英国1999年帕多瓦, 意大利1998年雷克雅未克, 冰岛1997年萨德伯里, 安大略省, 加拿大1996年奥斯陆, 挪威1995年堪培拉, 澳大利亚1994年北京, 中国1993年威廉斯堡, 佛吉尼亚州, 美国1992年赫尔辛基, 芬兰1991年哈瓦那, 古巴1990年格罗宁根, 荷兰1989年华沙, 波兰1988年 Bad Ischl, 奥地利1987年耶拿, 德意志民主共和国1986年伦敦Harrow, 英国1985年 Portorož, 南斯拉夫1984年 Sigtuna, 瑞典1983年布加勒斯特, 罗马尼亚1982年 Malente, 西德1981年瓦尔纳, 保加利亚1979年莫斯科, 苏联1977年赫拉德茨-克拉洛韦, 捷克斯洛伐克1976年布达佩斯, 匈牙利1975年居斯特罗, 东德1974年华沙, 波兰1972年布加勒斯特, 罗马尼亚1971年索非亚, 保加利亚1970年莫斯科, 苏联1969年布尔诺, 捷克斯洛伐克1968年布达佩斯, 匈牙利1967年华沙, 波兰。
约翰摩尔大学学费多少钱
约翰摩尔大学学费多少钱
英国利物浦约翰摩尔大学Non-lab programmes的学费是￡16，900；而Lab programmes的学费是￡17，400，今天店铺小编就给大家介绍约翰摩尔大学学费多少钱，如果对这个话题感兴趣的话，欢迎点击。
利物浦约翰摩尔大学课程学费
Non-lab programmes：￡16，900
Lab programmes：￡17，400
本科与研究生阶段的学费都按照此标准收费，如需了解具体的费用，请在申请的课程页面上进行查看。
利物浦约翰摩尔大学奖学金
今年利物浦约翰摩尔大学的奖学金政策已更新：
所有自费的国际本科生（包括开始读预科的学生）都可获得每学年3000英镑留学生成就奖学金。
所有申请授课型硕士课程的自费国际研究生都可以获得3000英镑的留学生成就奖学金。
*以上两项奖学金都将以减免学费的形式发放。
英国留学签证材料清单
1、2张近期护照照片（要求白色背景）
2、填写完整的签证申请表
3、署名签字的护照
4、正确的签证费
5、身份证原件，父母身份证原件
6、英国学校录取通知书原件
7、最高学历证书或在读证明及最近三年的成绩单——（中英文）
8、英语成绩证明（如IELTS，TOEFL）
9、出生公证或亲属关系公证书、户口本原件（中英文）
10、资金证明
11、至少六个月以上的资金来源证明
12、担保人在职/收入证明
13、担保人工资银行账户（如果适用）
14、担保人个人所得税税单
15、本人学习计划（英文）：写明赴英国学习的理由，将来的学习计划及学成后的打算
16、其他有利签证的文件（中英文）。
IF97 1区反推公式
Moscow, Russia June 2014
Revised Supplementary Release on Backward Equations for Pressure as a Function of Enthalpy and Entropy p(h,s) for Regions 1 and 2 of the IAPWS Industrial Formulation 1997 for the Thermodynamic Properties of Water and Steam
2
Contents
1 2 3 4 5 Nomenclature Background Numerical Consistency Requirements Structure of the Equation Set Backward Equation p(h,s) for Region 1 5.1 The Equation 5.2 Numerical Consistency with the Basic Equation of IAPWS-IF97 Backward Equations p(h,s) for Region 2 6.1 Subregions 6.2 The Equations 6.3 Numerical Consistency with the Basic Equation of IAPWS-IF97 6.4 Consistency at Boundaries Between Subregions Backward Functions T(h,s) for Regions 1 and 2 7.1 Calculation of the Backward Functions T(h,s) 7.2 Numerical Consistency with the Basic Equations of IAPWS-IF97 7.3 Consistency at Boundaries Between Subregions Computing Time in Relation to IAPWS-IF97 References 2 3 4 4 5 5 6 6 6 8 9 11 11 11 12 12 13 13
Performance Matched Discretionary Accrual Measures
Performance Matched Discretionary Accrual MeasuresS.P. KothariSloan School of ManagementMassachusetts Institute of Technology50 Memorial Drive, E52-325Cambridge, MA 02142kothari@Andrew J. LeoneWilliam E. Simon Graduate School of Business AdministrationUniversity of Rochester, Rochester, NY 14627leone@Charles E. WasleyWilliam E. Simon Graduate School of Business AdministrationUniversity of Rochester, Rochester, NY 14627wasley@First draft: October 2000Current draft: May 2001We gratefully acknowledge comments and suggestions of workshop participants at Arizona State University, the universities of Colorado and Rochester and especially from Wayne Guay and Jerry Zimmerman. S.P. Kothari acknowledges financial support from Arthur Andersen and Andy Leone and Charles Wasley acknowledge the financial support of the Bradley Policy Research Center at the Simon School and the John M. Olin Foundation.AbstractUsing discretionary accruals to test for earnings management and market efficiency is commonplace in the literature. We develop a well-specified (rejects the null hypothesis, when it’s true, at the test’s nominal significance level) and powerful (rejects a false null hypothesis with high probability) measure of discretionary accruals. A key feature of the discretionary accrual measure is that it is adjusted for the accrual performance of a matched firm where matching is on the basis of return on assets and industry. We advocate matching to control for the impact of performance on accruals. Our results suggest that performance matching is crucial to the design of well-specified tests based on discretionary accruals. Researchers will be able to draw more reliable inferences if they use a performance-matched discretionary accrual measure as proposed in this study.Performance Matched Discretionary Accrual Measures1.IntroductionUse of discretionary accruals in tests of earnings management and market efficiency is widespread (see, for example, Defond and Jiambalvo, 1994, Rees, Gill and Gore, 1996, Teoh, Welch, and Wong, 1998a and 1998b, and Kothari, 2001). In an influential study examining the specification and power of commonly used discretionary-accrual models, Dechow, Sloan, and Sweeney (1995, p. 193) conclude that “all models reject the null hypothesis of no earnings management at rates exceeding the specified test levels when applied to samples of firms with extreme financial performance.” Unfortunately, there has been little research since Dechow et al. (1995) on the properties of discretionary accrual models. Furthermore, and notwithstanding their conclusion above, the discretionary accrual models identified as misspecified continue to be used in research examining non-random samples (i.e., samples that firms self-select into by, for example, changing auditors).Our objective in this paper is to develop a discretionary-accrual estimation approach that is both well specified and powerful. Well-specified tests reject the null hypothesis, when it is true, at the nominal significance level of the test (e.g., 1% or 5%). In the context of discretionary accrual models, power of a test refers to the likelihood that a test concludes non-zero discretionary accruals of a given magnitude (e.g., 1%, 2%, etc.) in a sample of firms. Powerful tests reject the null hypothesis with high probability when it is false. A key feature of our study is that we examine properties of discretionary accruals adjusted for a performance-matched firm's discretionary accrual, where performance matching is on the basis of a firm’s return on assets for the past year and industry membership.Our results suggest that performance matching is crucial to designing well-specified tests of earnings management. The critical importance of controlling for the effect of pastperformance in tests of earnings management is not surprising. The simple model of earnings, cash flows, and accruals in Dechow, Kothari, and Watts (1998) shows that working capital accruals increase in forecasted sales growth and earnings because of a firm’s investment in working capital to support growth. Therefore, if a firm’s performance exhibits mean reversion or momentum (i.e., performance is not a random walk), then forecasted accruals would be non-zero. Firms with high growth opportunities often exhibit persistent growth patterns and accounting conservatism can produce earnings persistence in the presence of good news and mean reversion in the presence of bad news (B asu, 1997). In addition, there is evidence of mean reversion conditional on extreme earnings performance (see Brooks and Buckmaster, 1976, for early evidence on mean reversion). As a result, forecasted accruals of non-random samples of firms might be systematically non-zero.The correlation between performance and accruals is problematic in tests of earnings management because commonly used discretionary accrual models (e.g., the Jones and modified-Jones models) are severely mis-specified when applied to samples experiencing non-random performance (see Dechow, et al., 1995). Previous research therefore recommends and attempts to develop accrual models as a function of performance (see Kang and Sivaramakrishnan, 1995, Guay, et al., 1996, Healy, 1996, Dechow, Kothari, and Watts, 1998, Peasnell, Pope and Young, 2000, and Barth, Cram, and Nelson, 2001).We control for the impact of performance on estimated discretionary accruals using a performance-matched firm’s discretionary accrual. An alternative is to formally model accruals as a function of performance. To do so requires imposing a specific functional form linking accruals to past performance in the cross-section. Since a suitable way to do this is not immediately obvious, we develop a control for prior performance by using a performance-matched firm’s discretionary accrual. Using a performance-matched firm’s discretionary accrual does not impose any particular functional form linking accruals to performance in a cross-section of firms. Instead, the assumption underlying performance matching is that, at the portfolio level,the unspecified impact of performance on accruals is identical for the test and matched control samples. Results below suggest that tests using a performance-matched companion portfolio approach to estimate discretionary accruals are better specified than those using a regression-based approach (which imposes a linear functional form) to control for the effect of past performance on future accruals.We also study discretionary accrual models’ properties over multi-year horizons, for a range of sample sizes, and for many types of non-random samples (e.g., large vs. small firms, growth versus value stocks, high vs. low earnings yield stocks, high vs. low past sales growth, etc.) and with and without controlling for potential survivorship biases. These features are designed to mimic characteristics of typical research studies in accounting. Previous research (e.g., Dechow et al., 1995, and Guay, Kothari, and Watts, 1996) does not simulate test conditions like multi-year horizons, different sample sizes, or survivorship biases. Nor does it systematically examine properties of discretionary accruals adjusted for performance-matched firms’ discretionary accruals. While adjustment of discretionary accruals for those of performance-matched samples is not uncommon in the literature, researchers choose from a wide range of firm characteristics on which to match without systematic evidence to guide the choice of a matching variable. For example, previous research uses control firms matched on cash flows (Defond and Subramanyam, 1998), year and industry (Defond and Jiambalvo, 1994), industry and size (Perry and Williams, 1994), and control firm defined as the median performance of the subset of firms in the same industry with past performance similar to that of the treatment firm (Holthausen and Larcker, 1996) or median performance of the percentile of firms matched on return on assets (Kasznik, 1999).Summary of results. The main result from our simulation analysis is that discretionary accruals estimated using the Jones or the modified-Jones model and adjusted for a performance-matched firm’s discretionary accruals are quite well specified. We label these as performance-matched discretionary accruals. Performance matching is on the basis of industry and past year’sreturn on assets.1 Performance-matched discretionary accruals exhibit only a modest degree of mis-specification in certain non-random samples, but otherwise tests using them perform quite well. We reach this conclusion on the basis of analyzing random as well as non-random samples of firms, one- and multi-year measurement intervals, a wide range of sample sizes, and tests for both positive and negative discretionary accruals. We, however, caution the reader that non-random sample firms might be engaging in earnings management for contracting, political, and capital market reasons. Therefore, the well-specified rejection rate of the performance-matched approach might in fact indicate under-rejection of the null hypothesis (see Guay et al., 1996). Our result that performance-matched measures are well specified is nevertheless helpful insofar as a researcher calibrates discretionary accruals relative to those estimated for a matched sample that has not experienced the treatment event (also see section 2). Performance-matched measures’ superior performance compared to other measures of discretionary accruals parallels the result in the context of operating performance measures and long-horizon stock returns (see Barber and Lyon, 1996 and 1997, Lyon, Barber, and Tsui, 1999, and Ikenberry, Lakonishok, and Vermaelen, 1995).Other aspects of our findings are that rejection rates are quite similar across different non-random samples and are moderately higher as the sample size increases and as the horizon increases from one year to three or five years. For example, when the sample size is 100 firms and discretionary accruals equal 2% of assets, the tests conclude significant abnormal accruals approximately 50% of the time. The rejection frequency jumps to about 90% if the discretionary accruals are 4% of assets. Our rejection rates are considerably higher than those reported in Dechow et al. (1995). We believe that differences in research design account for the differences in the rejection rates reported in their versus our study. Specifically, Dechow et al. report the1 While other performance matching variables are possible, performance matching on the basis of lagged return on assets follows the approach taken in Barber and Lyon (1996) in their study of detecting abnormal operating performance. Barber and Lyon (1996) do not study accruals, discretionary or non-discretionary.percentage of times out of 1,000 samples of one firm each that the null hypothesis of no earnings management is rejected when a given level of discretionary accrual is introduced into the data. In comparison, we report rejection frequencies when sample sizes are 100 or more. Our rationale is straightforward. Invariably, researchers examine whether there is evidence of non-zero discretionary accruals, on average, in a sample of firms, not for a single firm.2 In contrast to Dechow et al.’s conclusion that all discretionary accrual models are misspecified and thus problematic when applied in actual research, our simulation results provide clear guidance to researchers in selecting an abnormal accrual measure in an actual empirical setting. More specifically, our findings suggest that researchers will be on firmer ground if they used a performance-matched accrual measure. Conversely, researchers who do not use such a measure are likely to draw inferences that are unreliable at best and incorrect at worst.To provide some evidence of the potential bias engendered by using discretionary accrual models without performance matching, we estimate discretionary accruals for a sample of firms making seasoned equity offers. In essence we replicate Teoh et al. (1998a) using measures of discretionary accruals (based on the Jones Model) with and without performance matching. The results clearly demonstrate that the magnitudes of discretionary accruals are substantially attenuated upon performance matching. Moreover, the inferences about the behavior of discretionary accruals around seasoned equity offerings that are drawn by Teoh et al (1998a) are not robust when performance-matched discretionary accruals are used.2 Another difference between Dechow et al. and our study is that they estimate a firm-specific time-series discretionary accrual model, whereas we estimate discretionary accruals using within-industry cross-sectional models. Firm-specific estimation imposes more stringent data requirements and thus biases the sample toward large firms, which means our samples likely consist of a greater proportion of smaller firms than Dechow et al. Since small firms’ accruals are more volatile, the power is expected to be lower (see our results in section 5). Therefore, the greater power in this study compared to Dechow et al. is notwithstanding the bias against such a finding due to the difference in sample selection procedures.Section 2 provides the motivation for using a performance-matched approach to develop well-specified tests of discretionary accruals and section 3 describes the simulation procedure. Section 4 summarizes the results on the specification of the test (i.e., rejection frequencies when the null hypothesis of zero abnormal performance is true) and section 5 reports results for the power of the test (i.e., rejection rates when we add 1% to 4% discretionary accrual to each sample firm’s estimated discretionary accrual). Section 6 reports the results of a wide range of sensitivity analyses. In section 7 we present the results of replicating a study examining discretionary accruals over a multi-year horizon following seasoned equity issues. Section 8 summarizes and discusses recommendations for future research.2.Motivation for performance matchingIn this section we describe the relation between firm performance and accruals. This provides a framework and the motivation for developing a control for firm performance when estimating discretionary accruals and for comparing estimated discretionary accruals between samples of firms. Economic intuition, extant models of accruals, earnings, and cash flows, and empirical evidence all suggest that accruals are correlated with a firm’s contemporaneous and past performance.3 While the Jones and modified-Jones discretionary accrual models attempt to control for contemporaneous performance on non-discretionary accruals, empirical assessments of these models suggest that estimated discretionary accruals are significantly influenced by a firm’s contemporaneous and past performance (e.g., Dechow, Sloan, and Sweeney, 1995).Properties of earnings, cash flows, and accruals. To formalize a relation between firm performance and accruals, we begin with a simple version of the model of earnings, cash flows, and accruals discussed in Dechow et al. (1998). Ignoring the depreciation accrual and assuming3 See, for example, Guay, Kothari, and Watts (1996), Healy (1996), Dechow, Kothari, and Watts (1998), Dechow, Sloan, and Sweeney (1995), and Barth, Cram, and Nelson (2001).(i) sales, S t, follow a random walk, (ii) cash margin of sales is a constant percentage π, (iii) αfraction of sales are on credit, and (iv) all expenses are cash, Dechow et al. show that CF t = π S t - α εt(1)A t = α εt, and(2)X t = CF t + α εt = π S t,(3) where CF is cash flow, A is accrual, εt = S t – S t-1 is change in sales (or sales shock if earnings follow a random walk), and X is accounting earnings. In this simple setting forecasted accruals are zero because sales follow a random walk. Moreover,E t(A t+1) = E t(α εt+1) = 0 , (4) and the forecast of future cash flows is current earnings,E t(CF t+1) = E t(π S t+1 - α εt+1) = π S t = X t. (5)The above analysis suggests that as long as the assumption of a random walk for sales, and therefore earnings, is descriptive of a sample of firms, forecasted accruals are zero.4 Moreover, since a random walk in sales and earnings is a reasonable assumption for a randomly selected sample of firms (see, for example, Ball and Watts, 1972), discretionary accrual models that do not include a good control for firm performance might still be well specified and lead to valid inferences. However, as seen from eq. (4), if forecasted sales changes are not zero (i.e., sales depart from a random walk) or when profit margins or other parameters affecting accruals change, then forecasted earnings changes as well as accruals are also non-zero. Forecasted sales and earnings changes can be positive or negative depending on whether the past performance is expected to be mean reverting or expected to exhibit momentum. Extreme one-time increases or4 This conclusion also holds for models that better capture the complexity of accounts payables and fixed costs (see Dechow et al., 1998). However, the result cannot be demonstrated as cleanly as in the case of the simple model we present here.decreases in performance are likely to produce mean reversion, whereas growth stocks might exhibit momentum for a period of time. Mean reversion or momentum in sales and earnings performance is quite likely for firms exhibiting unusual past performance. This predictability in future performance generates predictability in future accruals and unless the discretionary accrual models adequately filter out this performance-related predictable component of accruals, there is a danger of spurious indication of discretionary accruals. Previous research (e.g., Dechow et al., 1995, and Guay et al., 1996) suggests the likelihood of a spurious indication of discretionary accruals is extremely high in samples experiencing non-random past performance.Controlling for the effect of performance on accruals. One means of controlling for the influence of firm performance on estimated discretionary accruals is to develop better models of discretionary accruals that are immune to the effects of performance. In this spirit, we augment the Jones (1991) and modified-Jones discretionary models to include past return on assets. Another approach that we investigate is to adjust the Jones and modified-Jones model discretionary accrual of a given firm by subtracting the corresponding discretionary accrual on a firm matched on the basis of prior year return on assets.The choice of matching on past return on assets is guided by the modeling of earnings, cash flows, and accruals summarized above. In particular, eq. (4) for the prediction of accruals suggests that when sales changes are predictable, earnings changes will also be predictable and forecasted accruals will be non-zero.5 In samples of firms that are non-random with respect to prior firm performance, earnings changes are predictable and their accruals are also expected to be non-zero. Intuitively, either the inclusion of past firm performance as an explanatory variable in the discretionary accrual model or adjustment of a firm’s estimated discretionary accrual by5 As the simple model suggests, an alternative to return on assets would be to match on past sales growth. However, matching on return on assets serves to incorporate other factors contributing to the firm’s accrual generating process, which our simple model does not capture but are likely to affect the magnitude of nondiscretionary accruals.that of a (performance) matched firm would serve to mitigate the likelihood that the resulting estimated discretionary accruals would systematically be non-zero (i.e., lead to invalid inferences about accrual behavior). Selection of return on assets as a performance measure is logical because assets are typically used as a deflator in the discretionary accrual models and because earnings performance deflated by assets is (net) return on assets.The relative efficacy of including a performance variable in the discretionary accrual regression model versus the matched-firm approach is an empirical issue. The regression approach imposes either stationarity of the relation through time or in the cross-section and, perhaps more importantly, does not accommodate potential non-linearity in the relation between the magnitude of performance and accruals. It is well known that the mapping of current performance into future performance, or the mapping of performance into returns, is highly non-linear (e.g., Brooks and Buckmaster, 1976, Beaver, Clarke, and Wright, 1979, Freeman and Tse, 1992, and Basu, 1997). Unless the discretionary accrual models are modified to address non-linearity, the regression approach may be less effective at controlling for non-zero estimated discretionary accruals in non-random samples. Conversely, the matched-firm approach does not impose restrictions on the functional form of the relation between performance and accruals. Nonetheless, the success of the matched-firm approach hinges on the precision with which matching can be done and the homogeneity in the relation between performance and accruals for the matched firm and the sample firm.Does controlling for past performance over-correct for the problem? Use of industry and performance-matched control firms might remove discretionary accruals resulting from the treatment firms’ earnings management activities and thus the researcher might fail to reject the null hypothesis when it is false. The concern arises because matched firms in the industry might face similar incentives as the treatment firms and thus might have engaged in similar earnings management activities. While such a concern is not entirely misplaced, controlling for performance-related accruals is nevertheless warranted. In an earnings management event study,researchers typically infer whether an event (e.g., a seasoned equity offer) influences reported earnings performance in the pre- and post-event years. If the treatment firms’ earnings performance in the post-event period is indistinguishable from the control firms, then the conclusion would be that the firms experiencing the event do not engage in earnings management any more or less than the matched firms that do not experience the event. Of course, it is possible that both treatment and control firms engage in earnings management. However, this is not central to the researcher’s event study because the event study seeks to discern whether the event contributes to earnings management for reasons beyond other known or observable factors like past performance. Therefore, it behooves to match on performance in ascertaining whether an event influences reported earnings performance.3.Simulation procedureThis section describes the baseline simulation procedure that we use to provide evidence on the specification and power of the tests using alternative measures of discretionary accruals. We discuss non-random and random sample construction (section 3.1), discretionary accrual measures (section 3.2), and the test statistics under the null hypothesis of zero discretionary accruals (section 3.3). Section 3.4 presents descriptive statistics and serial correlation properties of all the discretionary accrual measures. These statistics provide a preliminary assessment of the potential biases in the discretionary accrual models, which contribute to test misspecifications.3.1Sample constructionWe begin with all 552,251 firm-year observations from the COMPUSTAT Industrial Annual and Research files from 1959 through 1998. From these, we exclude firm-year observations that do not have sufficient data to compute total accruals (described in section 2.2) or where the absolute value of total accruals scaled by total assets is greater than one. This reduces the sample to 172,973 firm-year observations. We next exclude all firm-yearobservations where there are fewer than ten observations in any two-digit SIC code in any given year. This is designed to exclude observations for which the regression-model-based discretionary accrual estimates are likely to be imprecise. This step reduces the sample to 170,197 observations. Finally, we do not allow simulated event dates past 1993 because in one set of simulations we examine accruals over a five-year horizon (e.g., accruals from 1994-1998 for samples selected in 1993). This reduces the number of usable firm-year observations for event-date simulations to 135,332. In much of our analysis, we match firms on the basis of performance (described below). The final sample size as a result of performance matching is 94,045 observations.We report baseline simulation results for 250 samples of 100 firms each. We draw samples without replacement either from the "all-firms" population or from non-random subsets of the population. The non-random subsets are firms in the lowest and highest quartiles of firms ranked on book-to-market ratio (BM), past sales growth (SG), earnings-to-price ratio (EP), and the market value of equity (Size). To construct these non-random subsets of the population, each year we rank all firm-year observations on the basis of each partitioning characteristic (e.g., BM or size) measured at the beginning of the year. We then pool across all the years the observations in the respective upper or lower quartile to obtain non-random subsets from which samples can be drawn. From each subset, we then randomly select 250 samples of 100 firms.3.2Discretionary accrual measuresPrevious research (e.g., Dechow et al.) suggests that among the various discretionary accrual models, the Jones (1991) and the modified-Jones models (see Dechow et al.) perform the best. We therefore use discretionary accruals estimated using these two models. We also estimate a discretionary current accrual measure (i.e., the discretionary portion of accruals without the depreciation accrual) that is increasingly being used in accounting research (see Teoh et al., 1998a and 1998b). We estimate the performance-matched Jones-model discretionaryaccrual as the difference between the Jones model discretionary accrual and the corresponding discretionary accrual for a performance-matched firm. We similarly estimate the performance-matched modified-Jones model discretionary accrual. To compare the effectiveness of performance matching, versus a regression-based approach, we estimate an additional discretionary accrual measure where we include the previous year’s return on assets (ROA) in the Jones-Model regression.Details of estimating the discretionary accrual models are as follows. We begin with total accruals (TA) defined as the change in non-cash current assets minus the change in current liabilities excluding the current portion of long-term debt minus depreciation and amortization.6The Jones model discretionary accrual is estimated cross-sectionally each year using all firm-year observations in the same two-digit SIC code.7TA it = β0/ΑSSETS it-1 + β1∆SALES it + β2PPE it + εit ,(6)where TA it, is total accruals as defined above, ∆SALES it is change in sales scaled by lagged total assets (ΑSSETS it-1), and PPE it is net property, plant and equipment scaled by ΑSSETS it-1.We use residuals from the annual cross-sectional industry regression model in (6) as the Jones model discretionary accruals. To obtain modified-Jones model discretionary accruals,following Dechow et al., we use the parameters from the Jones model eq. (6), but apply those to a modified sales change variable defined as (it SALES ∆- it AR ∆), where it AR ∆is the change in accounts receivable. Dechow et al.’s (1995) assume that sales are not managed in the estimation6 With reference to COMPUSTAT data items, total accruals = (∆Data4 - ∆Data1 - ∆Data5 + ∆Data34 -Data14)/lagged Data6. We scale total accruals by lagged total assets, Data6.7 While eq. (6) does not include an intercept, we repeat estimation of all models with an intercept in the regressions.The tenor of the results from the simulation analysis is unaffected.。
Tuition_Fees_and_Living_expenses_for_FT_100_MBAs
1.5+ 2 2 1 2 2 1+ 2 2 2 2 2 2 2 2 1 1 2 2 1 2 2 1+ 2 2 2 2 2 2 2 2 2 2 1 1 2 1 1+ 1 1 1 2 2 2 2 1 2 1 1.5.00 27,938.00 45,384.00 35,950.00 39,000.00 64,200.00 24,626.00 30,452.00 34,454.00 33,830.00 34,195.00 24,720.00 34,780.52 32,990.00 54,870.00 56,916.00 25,000.00 32,150.00 34,000.00 38,500.00 31,500.00 32,400.00 32,630.00 36,590.00 26,614.00 45,059.00 49,450.00 30,110.00 33,600.00 34,005.00 22,950.00 23,424.20 33,480.00 41,106.00 35,825.50 29,845.00 26,648.00 49,500.00 29,388.00 33,909.00 30,600.00 34,606.00 17,920.00 35,340.00 30,784.00 42,711.00 30,420.00 40,640.00 32,550.00
49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
Tuition fees and living expenses for the FT 100 MBAs
affect in language learning
Approaches and Methods in Language Teaching by Jack C. Richards and Theodore S. Rodgers
Appropriate Methodology and Social Context by Adrian Holliday Beyond Training by Jack C. Richards Collaborative Language Learning and Teaching edited by David Nunan Communicative Language Teaching by William Littlewood Communicative Methodology in Language Teaching by Christopher Brum®t Course Design by Fraida Dubin and Elite Olshtain Culture Bound edited by Joyce Merrill Valdes Designing tasks for the Communicative Classroom by David Nunan Developing Reading Skills by FrancËoise Grellet Developments in ESP by Tony Dudley-Evans and Maggie Jo St John Discourse Analysis for Language Teachers by Michael McCarthy Discourse and Language Education by Evelyn Hatch English for Academic Purposes by R.R. Jordan English for Speci®c Purposes by Tom Hutchinson and Alan Waters Focus on the Language Classroom by Dick Allwright and Kathleen M. Bailey Foreign and Second Language Learning by William Littlewood Language Learning in Intercultural Perspective edited by Michael Byram and
Formulation
C. Peters et al. (Eds.): CLEF 2005, LNCS 4022, pp. 792 – 799, 2006.© Springer-Verlag Berlin Heidelberg 2006Dublin City University at CLEF 2005: Cross-LanguageSpeech Retrieval (CL-SR) ExperimentsAdenike M. Lam-Adesina and Gareth J.F. JonesSchool of Computing, Dublin City University, Dublin 9, Ireland {adenike, gjones}@computing.dcu.ieAbstract. The Dublin City University participation in the CLEF 2005 CL-SRtask concentrated on exploring the application of our existing informationretrieval methods based on the Okapi model to the conversational speech dataset. This required an approach to determining approximate sentence boundarieswithin the free-flowing automatic transcription provided to enable us to use oursummary-based pseudo relevance feedback (PRF). We also performedexploratory experiments on the use of the metadata provided with the documenttranscriptions for indexing and relevance feedback. Topics were translated intoEnglish using Systran V3.0 machine translation. In most cases Title field onlytopic statements performed better than combined Title and Description topics.PRF using our adapted method is shown to be affective, and absoluteperformance is improved by combining the automatic document transcriptionswith additional metadata fields.1 IntroductionThe Dublin City University participation in the CLEF 2005 CL-SR task [1] concentrated on exploring the application of our existing information retrieval methods based on the Okapi model to this data set, and exploratory experiments on the use of the provided document metadata. Our official submissions included both the English monolingual and French bilingual runs. This paper reports additional results for German and Spanish bilingual runs. Topics were translated into English using the Systran V3.0 machine translation system. The resulting English topics were applied to the English document collection.Our standard Okapi retrieval system incorporates a summary-based pseudo relevance feedback (PRF) stage. This PRF system operates by selecting topic expansion terms from document summaries, full details are described in [2]. However, since the transcriptions of the conversational speech documents generated using automatic speech recognition (ASR) do not contain punctuation, we needed to develop a method of selecting significant document segments to identify documents “summaries”. Details of our method for doing this are described in Section 2.1.The spoken document transcriptions are provided with a rich set of metadata, further details are available in [1]. It is not immediately clear how best to exploit this most effectively in retrieval. This paper reports our initial exploratory experiments in making use of this additional information by merging it with the standard document transcriptions for indexing and relevance feedback.Dublin City University at CLEF 2005: CL-SR Experiments 793The remainder of this paper is structured as follows: Section 2 overviews ourretrieval system and describes our sentence boundary creation technique, Section 3presents the results of our experimental investigations, and Section 4 concludes thepaper with a discussion of our results.2 System SetupThe basis of our experimental system is the City University research distributionversion of the Okapi system [3]. The documents and search topics are processed toremove stopwords from a standard list of about 260 words, suffix stripped using theOkapi implementation of Porter stemming [4] and terms are indexed using a smallstandard set of synonyms. None of these procedures were adapted for the CLEF 2005CL-SR test collection. The documents fields to be indexed for a particular set ofexperiments were merged into a single document field prior to indexing.2.1 Term WeightingDocument terms were weighted using the Okapi BM25 weighting scheme developedin [3] calculated as follows,),()))(()1((*1)11(),()(),(j i tf j ndl b b K K j i tf i cfw j i cw +×+−+××=. where cw(i,j) represents the weight of term i in document j , cfw(i) is the standardcollection frequency weight, tf(i,j) is the document term frequency, and ndl(j) is thenormalized document length. ndl(j) is calculated as ndl(j) = dl(j)/avdl where dl(j) isthe length of j and avdl is the average document length for all documents. k1 and bare empirically selected tuning constants for a particular collection. k1 is designed tomodify the degree of effect of tf(i,j), while constant b modifies the effect of documentlength. High values of b imply that documents are long because they are verbose,while low values imply that they are long because they are multi-topic. The valuesused for our submitted runs were tuned using the provided training topics.2.2 Pseudo-relevance FeedbackWe apply PRF for query expansion using a variation of the summary-based methoddescribed in [2] which has been shown to be effective in our previous submissions toCLEF, including [5] and elsewhere. The main challenge for query expansion is theselection of appropriate terms from the assumed relevant documents. For the CL-SRtask our query expansion method operates as follows. A summary is made of the ASRtranscription of each of the top ranked documents, which are assumed to be relevantfor each PRF. Each document summary is then expanded to include all terms in themetadata fields used in this document index. All non-stopwords in these augmentedsummaries are ranked using a slightly modified version of the Robertson selectionvalue (rsv) [3] shown in equation (1).)()()(i rw i r i rsv ×=. (1)794 A.M. Lam-Adesina and G.J.F. Joneswhere r(i) = the total number of relevant documents containing term i , and rw(i) is thestandard Robertson/Sparck Jones relevance weight [3], )5.0)()(5.0)()(()5.0)()()(5.0)((log )(+−+−++−−+=i r R i r i n i r R i n N i r i rw where r(i) = is defined as before, n(i) = the total number of documents containingterm i , R = the total number of relevant documents for this query, and N = the totalnumber of documentsThe top ranked terms are then added to the topic. In our modified version of rsv(i),potential expansion terms are selected from the augmented summaries of the topranked documents, but ranked using statistics from a larger number of assumedrelevant ranked documents from the initial run.2.2.1 Sentence SelectionOur standard process for summary generation is to select representative sentencesfrom the document [6]. Since the transcriptions in the CL-SR document set do notcontain punctuation marking, we needed an alternative approach to identifyingsignificant units in the transcription. We approached this using a method derived fromLuhn’s word cluster hypothesis. Luhn’s hypothesis states that significant wordsseparated by not more than 5 non-significant words are likely to be strongly related.Clusters of these strongly related word were identified in the running documenttranscription by searching for word groups separated by not more than 5 insignificantwords, as shown in Figure 1. Note that words appearing between clusters are notincluded in clusters, but can be ignored for the purposes of query expansion since theyare by definition stop words. … this chapter gives a brief description of the [data sets used in evaluating theautomatic relevance feedback procedure investigated in this thesis ] and alsodiscusses the extension of …Fig. 1. Example of Sentence creationThe clusters were then awarded a significance score based on two measures.Luhn’s Keyword Cluster Method. Luhn‘s method assigns a sentence score for thehighest scoring cluster within a sentence. We adapted this method to assign a clusterscore as follows:TWSW SS 21=.where SS1 = the sentence scoreSW = the number of bracketed significant wordsTW = the total number of bracketed wordsFor the example in Fig. 1, SW =6 and TW =14. Query-Bias Method. This method assigns a score to each sentence based on thenumber of query terms in the sentence as follows:Dublin City University at CLEF 2005: CL-SR Experiments 795 NQTQ SS 22= .where SS2 = the sentence scoreTQ = the number of query terms present in the sentenceNQ = the number of terms in a query The overall score for each sentence (cluster) was then formed by summing thesetwo measures for each sentence.3 Experimental InvestigationThis section describes the establishment of the parameters for our experimentalsystem and then gives results from our investigations.3.1 Selection of System ParametersIn order to set the appropriate parameters for our feedback runs, we carried outdevelopment runs using the CLEF 2005 CL-SR training topics. The Okapi parameterswere set as follows k1=1.4 b =0.8. For all our PRF runs, 5 documents were assumedrelevant for term selection and document summaries comprised the best scoring 4clusters. The rsv values to rank the potential expansion terms were estimated based onthe top 20 or 40 ranked assumed relevant documents. The top 20 ranked expansionterms taken from the clusters were added to the original query in each case. Based onresults from our previous experiments in CLEF, the original topic terms are up-weighted by a factor of 3.5 relative to terms introduced by PRF. For our submittedruns we used either the Title section (dcu*tit) or the Title and Description (dcu*desc)section of each topic. Our official submitted runs are marked + the tables of results.Baseline monolingual results using English topics without query expansion are givenfor comparison for each experimental condition.For our experiments the document fields were combined as follows:dcua2 – combination of ASRTEXT2004A and AUTOKEYWORDA1dcua1a2 – combination of ASRTEXT2004A, AUTOKEYWORDA1 andAUTOKEYWORDA2dcusum – combination of ASRTEXT2004A, AUTOKEYWORDA1 andAUTOKEYWORDA2 and the SUMMARYdcuall – combination of ASRTEXT2004A, SUMMARY, NAME andMANUALKEYWORD3.2 Experimental ResultsTables 1-4 show results of our experiments using these different data combinationsfor the 25 test topics released for the CLEF 2005 CL-SR task. Results shown areMean Average Precision (MAP), total relevant documents retrieved (Rr), andprecision at cutoffs of 10 and 30 documents. Topic languages used are English,French, German and Spanish. Topics were translated into English using the SystranV3.0 machine translation system. The upper set of results in each table shows796 A.M. Lam-Adesina and G.J.F. JonesTable 1. Results using a combination of ASRTEXT2004A and AUTOKEYWORDA1, with theTitle or Title and Description topic fields. Expansion terms ranked for selection using statisticsof 40 top ranked documents.MAP Rr P10 P30 Run-id TopicLang.dcua2desc40f Baseline 0.050 536 0.148 0.103English 0.065+ 738 0.176 0.1400.1390.2080.076744French0.1160.0996110.041German0.1090.1520.055Spanish727dcua2tit40f Baseline 0.070 384 0.228 0.1430.1510.2520.080622English0.2520.1550.081708French0.1840.1206470.056German6020.1290.1920.068Spanishcombined Title and Description topic queries and the lower set Title only topicqueries.Results in Table 1 show results for combination of ASRTEXT2004A with AUTOKEYWORDA1. It can be seen that the PRF method improves results for theEnglish topics in each case. Also that the results using Title only topics are better thanthose using the combined Title and Description topics with respect to MAP. Thisresult is perhaps a little surprising since the latter are generally found to be performbetter and we are investigating the reasons for the results observed here. However, thenumber of relevant documents retrieved is generally higher when using the combinedtopics which is to be expected since the topics will contain more terms which canmatch with potentially relevant documents. Cross-language information retrieval(CLIR) results using French topics are shown to perform better than monolingualTable 2. Results using a combination of ASRTEXT2004A, AUTOKEYWORDA1 and AUTOKEYWORDA2, with the Title or Title and Description topic fields. Expansion termsranked for selection using statistics of 40 top ranked documents.Run-id TopicMAP Rr P10 P30Lang.dcua1a2desc40f Baseline 0.046 500 0.188 0.105English 0.067 784 0.184 0.148 French0.1710.2167730.094German 0.046 611 0.096 0.0920.1647650.128Spanish0.064dcua1a2tit40f Baseline 0.0800 472 0.228 0.1600.110+ 727 0.252 0.196English0.106+ 768 0.260 0.191FrenchGerman 0.074 691 0.172 0.1490.2200.1560.091Spanish679Dublin City University at CLEF 2005:CL-SR Experiments 797English for both MAP and relevant retrieved. This is again unusual, but not unprecedented in CLIR. Results for translated German and Spanish topics show areduction compared to the monolingual results.Table 2 shows results for the same set of experiments as those in Table 1 with theaddition of the AUTOKEYWORDA2 metadata to the documents. Results heregenerally show similar trends to those in Table 1 with small absolute increases in performance in most cases. In this case the performance advantage of French topicsover English topics with PRF has largely disappeared for the Title only topics,however, performance for French topics is still much better than for English topics forthe combined Title and Description topics.Table 3. Results using a combination of ASRTEXT2004A, AUTOKEYWORDA1 and AUTOKEYWORDA2 and the SUMMARY section of each document, with the Title or Titleand Description topic fields. Expansion terms ranked for selection using statistics of 40 topranked documents.MAP Rr P10 P30Run-id TopicLang.dcusumdesc40f Baseline 0.105 598 0.224 0.171English 0.147 889 0.272 0.2170.2608560.2160.154French0.1640.1376960.108German0.1688600.152 Spanish0.107dcusumtit40f Baseline 0.141 618 0.284 0.2160.2430.2920.167770English0.165+ 837 0.308 0.251French0.160 German0.2207380.1107360.1300.2840.154SpanishTable 3 shows results for a further set of experiments with the SUMMARY fieldadded to the document descriptions. All results here show large increases compared tothose in Table 2, indicating that the contents of the SUMMARY field are useful descriptions of the documents. The SUMMARY of each document is manuallygenerated and presumably includes important terms which may be good descriptionsof the topic of the document and possibly words actually appearing in the document,but incorrectly transcribed by the speech recognition system. The relative performance of monolingual and cross-language topics is the same as that observed inTable 2.Table 4 shows a final set of experiments combining the ASRTEXT2004A, SUMMARY, NAME and MANUALKEYWORD fields. These results show large improvements over the results shown in previous tables. Performance for Title onlyand Title and Description combined topics is now similar with neither clearlyshowing an advantage. Monolingual English performance is now clearly better thanresults for translated French topics for both topic types, while our PRF method is stillshown to be effective. The manually assigned keywords are shown to be particularlyuseful additional search fields.798 A.M. Lam-Adesina and G.J.F. JonesTable 4. Results using a combination of ASRTEXT2004A, SUMMARY, NAME and MANUALKEYWORD section of each document, with the Title or Title and Description topicfields. Expansion terms ranked for selection using statistics of 40 top ranked documents.Run-id TopicMAP Rr P10 P30Lang.dcualldesc40f Baseline 0.221 1031 0.368 0.271English 0.283 1257 0.432 0.3370.4240.30311220.257French0.32810010.2720.229German0.2970.38011600.247Spanishdcualltit40f Baseline 0.242 736 0.412 0.3110.4880.3770.3071009English0.4960.36011360.276French0.2760.360German9620.2050.3600.2680.232Spanish9084 Conclusions and Further WorkOur initial experiments with the CLEF 2005 CL-SR task illustrate that PRF can be successfully applied to this data set, and that the different fields of the document setmake varying levels of positive contribution to information retrieval effectiveness. Ingeneral in can be seen that manual assigned fields are more useful than the automatically generated ones.These experiments only represent a small subset of those that are possible with thisdataset. In order to better understand the usefulness of document fields and retrievalmethods more detailed analysis of these existing results and further experiments are planned. The okapi retrieval model generally produces competitive retrieval results. However, in this case the results achieved are significantly lower than those observedusing a parameter setting of the SMART retrieval system [7]. It is important to understand why the standard okapi weighting does not appear to work well with theCLEF 2005 CL-SR test collection, and we will be pursuing this issue as part of ourfurther work.References1.White, R. W., Oard, D. W., Jones, G. J. F., Soergel, D., and Huang, X.: Overview of theCLEF-2005 Cross-Language Speech Retrieval Track,Proceedings of the CLEF 2005:Workshop on Cross-Language Information Retrieval and Evaluation,Vienna, Austria, 2005.m-Adesina, A. M., and Jones, G. J. F.: Applying Summarization Techniques for TermSelection in Relevance Feedback, Proceedings of the Twenty-Fourth Annual InternationalACM SIGIR Conference on Research and Development in Information Retrieval, pages 1-9,New Orleans, 2001. ACM.3.Robertson, S. E., Walker, S., Jones, S., Hancock-Beaulieu, M. M. ,and Gatford, M.: Okapiat TREC-3, Proceedings of the Third Text REtrieval Conference (TREC-3), pages 109-126.NIST, 1995.Dublin City University at CLEF 2005:CL-SR Experiments 7994.Porter, M. F.: An Algorithm for Suffix Stripping, Program, 14:10-137, 1980.5.Luhn. H.P.: The Automatic Creation of Literature Abstracts. IBM Journal of Research andDevelopment, 2(2):159-165, 1958.6.Jones, G. J. F., Burke, M., Judge, J., Khasin, A., Lam-Adesina, A. M., and Wagner, J.:Dublin City University at CLEF 2004: Experiments in Monolingual, Bilingual and Multilingual Retrieval, Proceedings of the CLEF 2004: Workshop on Cross-Language Information Retrieval and Evaluation, Bath, U.K., pages 207-220, 2004.7.Tombros, A., and Sanderson, M.: The Advantages of Query-Biased Summaries inInformation Retrieval. In proceedings of the Twenty-First Annual International ACM SIGIR Conference Research and Development in Information Retrieval, pages 2-10, Melbourne, 1998. ACM.8.Inkpen, D., Alzghool, M., and Islam, A. : University of Ottawa’s Contribution to CLEF2005, the CL-SR Track Proceedings of the CLEF 2005: Workshop on Cross-Language Information Retrieval and Evaluation, Vienna, Austria, 2005.。
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利物浦约翰摩尔斯大学奖学金种类和申请条件.doc
利物浦约翰摩尔斯大学奖学金种类和申请条件利物浦约翰摩尔斯大学(Liverpool John MooresUniversity)建于1823年，是英国规模最大的大学之一。
学校目前有15个学院，专业涉及艺术、生物地质学、生物分子学、生态环境学、商学、计算机及数学、教育及社会研究学、工程、人文科学、法律及应用社会学、传统及抽象艺术等领域。
其中，物理学与天文学、运动与旅游专业是其优势专业。
利物浦约翰摩尔斯大学目前可以申请的奖学金主要有志奋领奖学金和体育奖学金。
具体内容请看以下的介绍。
Scholarships for international applicantsIf you’re an outstanding international student, you could receive one ofthe following:an external scholarship,a sports scholarship.利物浦约翰摩尔斯大学国际申请人奖学金如果你是国际生，并且表现突出，你可以获得以下一项奖学金：(1)外部奖学金; (2)体育奖学金。
External scholarships外部奖学金1.Science without BordersAward: Tuition fees and accommodation costsWhat is it?Science without Borders (Ciência sem Fronteiras) is a Brazilian governmentscholarship programme. The programme aims to send 101,000 Brazilian students onundergraduate sandwich courses, PhD sandwich courses andfull PhDs to study abroad range of subjects in top universities around the world.Am I eligible to apply?You can apply if you are:a Brazilian studentplanning to study an undergraduate sandwich programmes at Liverpool JohnMoores University over the next four years科学无国界奖学金奖学金金额：相当于学费和生活费。
Analysisofrepresentationsfordomainadaptation
AnalysisofrepresentationsfordomainadaptationShai Ben-David School of Computer Science University of Waterlooshai@cs.uwaterloo.ca John Blitzer,Koby Crammer,and Fernando Pereira Department of Computer and Information Science University of Pennsylvania{blitzer,crammer,pereira}@/doc/d3bd993987c24028915fc339.htmlAbstractDiscriminative learning methods for classi?cation perform well when training andtest data are drawn from the same distribution.In many situations,though,wehave labeled training data for a source domain,and we wish to learn a classi?erwhich performs well on a target domain with a different distribution.Under whatconditions can we adapt a classi?er trained on the source domain for use in thetarget domain?Intuitively,a good feature representation is a crucial factor in thesuccess of domain adaptation.We formalize this intuition theoretically with ageneralization bound for domain adaption.Our theory illustrates the tradeoffs in-herent in designing a representation for domain adaptation and gives a new justi?-cation for a recently proposed model.It also points toward a promising new modelfor domain adaptation:one which explicitly minimizes the difference between thesource and target domains,while at the same time maximizing the margin of thetraining set.1IntroductionWe are all familiar with the situation in which someone learns to perform a task on training examples drawn from some domain(the source domain),but then needs to perform the same task on a related domain(the target domain).In this situation,we expect the task performance in the target domain to depend on both the performance in the source domain and the similarity between the two domains. This situation arises often in machine learning.For example,we might want to adapt for a new user(the target domain)a spam?lter trained on the email of a group of previous users(the source domain),under the assumption that users generally agree on what is spam and what is not.Then,the challenge is that the distributions of emails for the?rst set of users and for the new user are different. Intuitively,one might expect that the closer the two distributions are,the better the?lter trained on the source domain will do on the target domain.Many other instances of this situation arise in natural language processing.In general,labeled data for tasks like part-of-speech tagging,parsing,or information extraction are drawn from a limited set of document types and genres in a given language because of availability,cost,and project goals. However,applications for the trained systems often involve somewhat different document types and genres.Nevertheless,part-of-speech,syntactic structure,or entity mention decisions are to a large extent stable across different types and genres since they depend on general properties of the language under consideration.Discriminative learning methods for classi?cation are based on the assumption that training and test data are drawn from the same distribution.This assumption underlies both theoretical estimates of generalization error and the many experimental evaluations of learning methods.However,the as-sumption does not hold for domain adaptation[5,7,13,6].For the situations we outlined above,the challenge is the difference in instance distribution between the source and target domains.We will approach this challenge by investigating how a common representation between the two domainscan make the two domains appear to have similar distributions,enabling effective domain adapta-tion.We formalize this intuition with a bound on the target generalization error of a classi?er trained from labeled data in the source domain.The bound is stated in terms of a representation function,and it shows that a representation function should be designed to minimize domain divergence,as well as classi?er error. While many authors have analyzed adaptation from multiple sets of labeled training data[3,5,7, 13],our theory applies to the setting in which the target domain has no labeled training data,butplentiful unlabeled data exists for both target and source domains.As we suggested above,this setting realistically captures the problems widely encountered in real-world applications of machine learning.Indeed recent empirical work in natural language processing[11,6]has been targeted atexactly this setting.We show experimentally that the heuristic choices made by the recently proposed structural corre-spondence learning algorithm[6]do lead to lower values of the relevant quantities in our theoreticalanalysis,providing insight as to why this algorithm achieves its empirical success.Our theory also points to an interesting new algorithm for domain adaptation:one which directly minimizes a trade-off between source-target similarity and source training error.The remainder of this paper is structured as follows:In the next section we formally de?ne domainadaptation.Section3gives our main theoretical results.We discuss how to compute the bound in section4.Section5shows how the bound behaves for the structural correspondence learning representation[6]on natural language data.We discuss our? ndings,including a new algorithm fordomain adaptation based on our theory,in section6and conclude in section7.2Background and Problem SetupLet X be an instance set.In the case of[6],this could be all English words,together with the possible contexts in which they occur.Let Z be a feature space(R d is a typical choice)and{0,1} be the label set for binary classi?cation1.A learning problem is speci?ed by two parameters:a distribution D over X and a(stochastic)targetfunction f:X→[0,1].The value of f(x)corresponds to the probability that the label of x is 1.A representation function R is a function which maps instances to features R:X→Z.A representation R induces a distribution over Z and a(stochastic)target function from Z to[0,1]asfollows:[B]def=Pr D R?1(B)Pr?Df(z)def=ED[f(x)|R(x)=z]for any A?Z such that R?1(B)is D-measurable.In words,the probability of an event B under ?D is the probability of the inverse image of B under R according to D,and the probability that the label of z is1according to?f is the mean of probabilities of instances x that z represents.Note that?f(z)may be a stochastic function even if f(x)is not.This is because the function R can map two instances with different f-labels to the same feature representation.In summary,our learning setting is de?ned by? xed but unknown D and f,and our choice of representation function R and hypothesis class H?{g:Z→{0,1}}of deterministic hypotheses to be used to approximate the function f.2.1Domain AdaptationWe now formalize the problem of domain adaptation.A domain is a distribution D on the instance set X.Note that this is not the domain of a function.To avoid confusion,we will always mean a speci?c distribution over the instance set when we say domain.Unlike in inductive transfer,where the tasks we wish to perform may be related but different,in domain adaptation we perform the same task in multiple domains.This is quite common in natural language processing,where we might be performing the same syntactic analysis task,such as tagging or parsing,but on domains with very different vocabularies[6,11]. We assume two domains,a source domain and a target domain.We denote by D S the source distribution of instances and?D S the induced distribution over the feature space Z.We use parallel notation,D T,?D T,for the target domain.f:X→[0,1]is the labeling rule,common to both domains,and?f is the induced image of f under R.A predictor is a function,h,from the feature space,Z to[0,1].We denote the probability,according the distribution D S,that a predictor h disagrees with f byS(h)=Ez～?D S E y～?f(z)[y=h(z)]=Ez～?D S ?f(z)?h(z) .Similarly,?T(h)denotes the expected error of h with respect to D T.3Generalization Bounds for Domain AdaptationWe now proceed to develop a bound on the target domain generalization performance of a classi?er trained in the source domain.As we alluded to in section1,the bound consists of two terms.The?rst term bounds the performance of the classi?er on the source domain.The second term is a measure of the divergence between the induced source marginal?D S and the induced target marginal?D T.A natural measure of divergence for distributions is the L1or variational distance.This is de?ned asd L1(D,D′)=2supB∈B|Pr D[B]?Pr D′[B]|where B is the set of measureable subsets under D and D′.Unfortunately the variational distance between real-valued distributions cannot be computed from?nite samples[2,9]and therefore is not useful to us when investigating representations for domain adaptation on real-world data.A key part of our theory is the observation that in many realistic domain adaptation scenarios,we do not need such a powerful measure as variational distance.Instead we can restrict our notion of domain distance to be measured only with respect to function in our hypothesis class.3.1The A-distance and labeling function complexityWe make use of a special measure of distance between probability distributions,the A-distance,as introduced in[9].Given a domain X and a collection A of subsets of X,let D,D′be probability distributions over X,such that every set in A is measurable with respect to both distributions.the A-distance between such distributions is de?ned asd A(D,D′)=2supA∈A|Pr D[A]?Pr D′[A]|In order to use the A-distance,we need to limit the complexity of the true function f in terms of our hypothesis class H.We say that a function?f:Z→[0,1]isλ-close to a function class H with respect to distributions?D S and?D T ifinfh∈H[?S(h)+?T(h)]≤λ.A function?f isλ-close to H when there is a single hypothesis h∈H which performs well on both domains.This embodies our domain adaptation assumption,and we will assume will assume that our induced labeling function?f isλ-close to our hypothesis class H for a smallλ.We brie?y note that in standard learning theory,it is possible to achieve bounds with no explicit as-sumption on labeling function complexity.If H has bounded capacity(e.g.,a?nite VC-dimension), then uniform convergence theory tells us that whenever?f is notλ-close to H,large training samples have poor empirical error for every h∈H.This is not the case for domain adaptation.If the training data is generated by some D S and we wish to use some H as a family of predictors for labels in the target domain,T,then one can construct a function which agrees with some h∈H with respect to?D S and yet is far from H with respect to?D T.Nonetheless we believe that such examples do not occur for realistic domain adaptation problems whenthe hypothesis class H is suf?ciently rich, since for most domain adaptation problems of interest the labeling functionis’similarly simple’for both the source and target domains.3.2Bound on the target domain errorWe require one last piece of notation before we state and prove the main theorems of this work:the correspondence between functions and characteristic subsets.For a binary-valued function g(z),we let Z g?Z be the subset whose characteristic function is gZ g={z∈Z:g(z)=1}.In a slight abuse of notation,for a binary function class H we will write d H(·,·)to indicate the A-distance on the class of subsets whose characteristic functions are functions in H.Now we can state our main theoretical result.Theorem1Let R be a?xed representation function from X to Z and H be a hypothesis space of VC-dimension d.If a random labeled sample of size m is generated by applying R to a D S-i.i.d. sample labeled according to f,then with probability at least1?δ,for every h∈H:T(h)≤S(h)+ m d log2emδ +d H(?D S,?D T)+λwhere e is the base of the natural logarithm.Proof:Let h?=argmin h∈H(?T(h)+?S(h)),and letλT andλS be the errors of h?with respect to D T and D S respectively.Notice thatλ=λT+λS.T(h)≤λT+Pr D[Z h?Z h?]T[Z h?Z h?]+|Pr D S[Z h?Z h?]?Pr D T[Z h?Z h?]|≤λT+Pr DS[Z h?Z h?]+d H(?D S,?D T)≤λT+Pr DS≤λT+λS+?S(h)+d H(?D S,?D T)≤λ+?S(h)+d H(?D S,?D T)The theorem now follows by a standard application Vapnik-Chervonenkis theory[14]to bound the true?S(h)by its empirical estimate??S(h).Namely,if S is an m-size.i.i.d.sample,then with probability exceeding1?δ,S(h)≤S(h)+ m d log2emδm′m d+log4d log(2m′)+log(4Let us brie?y examine the bound from theorem2,with an eye toward feature representations,R. Under the assumption of subsection3.1,we assume thatλis small for reasonable R.Thus the two main terms of interest are the?rst and fourthterms,since the representation R directly affects them. The?rst term is the empirical training error.The fourth term is the sample A-distance between domains for hypothesis class H.Looking at the two terms,we see that a good representation R is one which achieves low values for both training error and domain A-distance simultaneously.4Computing the A-distance for Signed Linear Classi?ersIn this section we discuss practical considerations in computing the A-distance on real data.Ben-David et al.[9]show that the A-distance can be approximated arbitrarily well with increasing sample size.Recalling the relationship between sets and their characteristic functions,it should be clear thatcomputing the A-distance is closely related to learning a classi?er.In fact they are identical.The set A h∈H which maximizes the H-distance between?D S and?D T has a characteristic function h.Then h is the classi?er which achieves minimum error on the binary classi?cation problem ofdiscriminating between points generated by the two distributions.To see this,suppose we have two samples?U S and?U T,each of size m′from?D S and?D T respectively. De?ne the error of a classi?er h on the task of discriminating between points sampled from different distributions as1err(h)=theorem2that random projections approximate well distances in the original high dimensional space,as long as d is suf?ciently large.Arriaga and Vempala[1]show that one can achieve good prediction with random projections as long as the margin is suf?ciently large.5.2Structural Correspondence LearningBlitzer et al.[6]describe a heuristic method for domain adaptation that they call structural corre-spondencelearning(henceforth also SCL).SCL uses unlabeled data from both domains to induce correspondences among features in the two domains.Its?rst step is to identify a small set of domain-independent“pivot”features which occur frequently in the unlabeled data of both domains.Other features are then represented using their relative co-occurrence counts with these pivot features.Fi-nally they use a low-rank approximation to the co-occurence count matrix as a projection matrix P. The intuition is that by capturing these important correlations,features from the source and target domains which behave similarly for PoS tagging will be represented similarly in the projected space.5.3ResultsWe use as our source data set100sentences(about2500words)of PoS-tagged Wall Street Journal text.The target domain test set is the same set as in[6].We use one million words(500thousand from each domain)of unlabeled data to estimate the A-distance between the?nancial and biomedi-cal domains.The results in this section are intended to illustrate the different parts of theorem2and how they can affect the target domain generalization error.We give two types of results.The?rst are pictorial and appear in?gures1(a),1(b)and2(a).These are intended to illustrate either the A-distance(?gures 1(a)and2(a))or the empirical error(?gure1(b))for different representations.The second type are empirical and appear in2(b).In this case we use the Huber loss as a proxy from the empirical training error.Figure1(a)shows one hundred random instances projected onto the space spanned by the best two discriminating projections from the SCL projection matrix for part of the?nancial and biomedical dataset.Instances from the WSJ are depicted as?lled red squares,whereas those from MEDLINE are depicted as empty blue circles.An approximating linear discrimnator is also shown.Note, however,that the discriminator performs poorly,and recall that if the best discriminator performs poorly the A-distance is low.On the other hand,?gure1(b)shows the best two discriminating components for the task of discriminating between nouns and verbs.Note that in this case,a good discriminating divider is easy to?nd,even in such a low-dimensional space.Thus these pictures lead us to believe that SCL?nds a representation which results both in small empirical classi?cation error and small A-distance.In this case theorem2predicts good performance.(a)Plot of random projections repre-sentation for ?nancial (squares)vs.(b)Comparison of bound terms vs.target domain error for different choices of representation.Reprentations linear projections of the original feature space.Hu-loss is the labeled training loss after training,andA -distance is approximated as described in thesubsection.Error refers to tagging error forfull tagset on the target domain.Representation A -distance Error0.003Random Proj 0.2230.5610.077ConclusionsWe presented an analysis of representations for domain adaptation.It is reasonable to think that agood representation is the key to effective domain adaptation,and our theory backs up that intuition.Theorem2gives an upper bound on the generalization of a classi?er trained on a source domain and applied in a target domain.The bound depends on the representation and explicitly demonstrates thetradeoff between low empirical source domain error and a small difference between distributions. Under the assumption that the labeling function?f is close to our hypothesis class H,we can compute the bound from?nite samples.The relevant distributional divergence term can be written as the A-distance of Kifer et al[9].Computing the A-distance is equivalent to?nding the minimum-errorclassi?er.For hyperplane classi?ers in R d,this is an NP-hard problem,but we give experimental evidence that minimizing a convex upper bound on the error,as in normal classi?cation,can give a reasonable approximation to the A-distance.Our experiments indicate that the heuristic structural correspondence learning method[6]does infact simultaneously achieve low A-distance as well as a low margin-based loss.This provides a justi?cation for the heuristic choices of SCL“pivots”.Finally we note that our theory points to an interesting new algorithm for domain adaptation.Instead of making heuristic choices,we are investigating algorithms which directly minimize a combination of the A-distance and the empirical training margin.References[1]R.Arriaga and S.Vempala.An algorithmic theory of learning robust concepts and randomprojection.In FOCS,volume40,1999.[2]T.Batu,L.Fortnow,R.Rubinfeld,W.Smith,and P.White.Testing that distributions are close.In FOCS,volume41,pages259–269,2000.[3]J.Baxter.Learning internal representations.In COLT’95:Proceedings of the eighth annualconference on Computational learning theory,pages311–320,New York,NY,USA,1995. [4]S.Ben-David,N.Eiron,and P.Long.On the dif?culty of approximately maximizing agree-ments.Journal of Computer and System Sciences,66:496–514,2003.[5]S.Ben-David and R.Schuller.Exploiting task relatedness for multiple task learning.InCOLT2003:Proceedings of the sixteenth annual conference on Computational learning the-ory,2003.[6]J.Blitzer,R.McDonald,and F.Pereira.Domain adaption with structural correspondencelearning.In EMNLP,2006.[7]K.Crammer,M.Kearns,and J.Wortman.Learning from data of variable quality.In NeuralInformation Processing Systems(NIPS),Vancouver,Canada,2005.[8]W.Johnson and J.Lindenstrauss.Extension of lipschitz mappings to hilbert space.Contem-porary Mathematics,26:189–206,1984.[9]D.Kifer,S.Ben-David,and J.Gehrke.Detecting change in data streams.In Very LargeDatabases(VLDB),2004.[10]C.Manning.Foundations of Statistical Natural Language Processing.MIT Press,Boston,1999.[11]D.McClosky,E.Charniak,and M.Johnson.Reranking and self-training for parser adaptation.In ACL,2006.[12]M.Sugiyama and K.Mueller.Generalization error estimation under covariate shift.In Work-shop on Information-Based Induction Sciences,2005.[13]Y.W.Teh,M.I.Jordan,M.J.Beal,and D.M.Blei.Sharing clusters among related groups:Hierarchical Dirichlet processes.In Advances in Neural Information Processing Systems,vol-ume17,2005.[14]V.Vapnik.Statistical Learning Theory.John Wiley,New York,1998.[15]T.Zhang.Solving large-scale linear prediction problems with stochastic gradient descent.InICML,2004.。
美国认可雅思成绩的大学和机构
美国认可雅思成绩的大学和机构现在，美国越来越多的大学开始承认IELTS。
例如著名的加里福尼亚大学的各间分校，都接受IELTS成绩。
下面列出美国在IELTS中心注册的大学名单。
学校首字母查询 A B C D E F G H I J K L M N O P Q R S T U V W X Y ZAbilene Christian UniversityWeb: /Min IELTS Band Score:Undergraduate and Graduate AdmissionsAlbright CollegeWeb: /main.htmlMin IELTS Band Score: Undergraduate AdmissionsAllentown Business SchoolWeb: /index.aspAlliant International UniversityWeb: Min IELTS Band Score:Undergraduate and Graduate admissionsAmerican Association of Veterinary State Boards, Program for the Assessment of Veterinary Education Equivalence (PAVE)Web: American InterContinental University, BuckheadWeb: /Min IELTS Band Score: Undergraduate admissionsAmerican InterContinental University, DunwoodyWeb: /about.aspMin IELTS Band Score: Undergraduate and graduate admissions American InterContinental University, Ft. LauderdaleWeb: /Min IELTS Band Score: Undergraduate and graduate admissions American InterContinental University, HoustonAmerican InterContinental University, Los AngelesWeb: /Min IELTS Band Score: Undergraduate and graduate admissionsAmerican UniversityWeb: Min IELTS Band Score:Undergraduate admissionsAmerican University, Washington College of LawWeb: /Min IELTS Band Score: Graduate admissionsAmerican Veterinary Medical Association, Educational Commission for Foreign Veterinary Graduates (ECFVG)Web: Aquinas CollegeWeb: /Min IELTS Band Score: Undergraduate AdmissionsArdmore Higher Education ProgramWeb: Min IELTS Band Score:Undergraduate and graduate admissions for East Central University (Ada, OK), Murray State College (Tishomingo, OK), Southeastern Oklahoma State University (Durant, OK), and Oklahoma State University - Oklahoma City.Arizona State Board of NursingWeb: Arkansas State UniversityWeb: Min IELTS Band Score: Undergraduate AdmissionsArt Institute of AtlantaWeb: /programs.aspArt Institute of Boston at Lesley UniversityWeb: /aib/noflash_main.htmlMin IELTS Band Score:Undergraduate admissionsArt Institute of CaliforniaWeb: /Art Institute of CharlotteWeb: /Art Institute of ColoradoWeb: /index.aspArt Institute of DallasWeb: /Art Institute of Ft. LauderdaleWeb: /Art Institute of HoustonWeb: /Art Institute of Los AngelesWeb: /Art Institute of Los Angeles-Orange CountyWeb: /news_detail.asp?PressID=123Art Institute of MinnesotaWeb: /Art Institute of New York CityWeb: /Art Institute of PhiladelphiaWeb: /Art Institute of PhoenixWeb: /default.aspArt Institute of PittsburghWeb: /index2.aspArt Institute of SeattleWeb: /Art Institute of WashingtonWeb: /Art Institutes International at PortlandWeb: /index.aspAsbury Theological SeminaryWeb: /prospective/admiss_req.shtmlMin IELTS Band Score:Graduate admissionsAshland UniversityWeb: /Min IELTS Band Score: Undergraduate and graduate admissionsAssumption CollegeWeb: Min IELTS Band Score:Graduate admissionsAtlantic Culinary Academy at McIntosh CollegeWeb: /Bastyr UniversityWeb: Min IELTS Band Score: Undergraduate and Graduate AdmissionsBellevue Community CollegeWeb: /Benedictine CollegeWeb: Min IELTS Band Score: Undergraduate AdmissionsBentley CollegeWeb: Min IELTS Band Score:Undergraduate admissionsBerea CollegeWeb: Min IELTS Band Score: Undergraduate AdmissionsBerklee College of MusicWeb: Min IELTS Band Score: Undergraduate and Graduate AdmissionsBethany Lutheran CollegeWeb: /index.asp?bhcp=1Min IELTS Band Score: Undergraduate AdmissionsBoston UniversityWeb: /admissionsMin IELTS Band Score: Undergraduate and Graduate AdmissionsBoston University, School of Public HealthWeb: /sphMin IELTS Band Score: Graduate AdmissionsBradley UniversityWeb: /gradMin IELTS Band Score: Graduate AdmissionsBrandeis International Business SchoolWeb: /globalMin IELTS Band Score: Graduate AdmissionsBrandeis UniversityWeb: /admissions/Min IELTS Band Score: Undergraduate AdmissionsBrandeis University, Heller Graduate School, SID ProgramWeb: /sidMin IELTS Band Score:Graduate admissionsBrandeis University, SLIFKA Program in Intercommunal Coexistence Web: /programs/Slifka/Min IELTS Band Score:Graduate admissionsBriarcliffe College, BethpageWeb: /index.aspMin IELTS Band Score: Undergraduate AdmissionsBriarcliffe College, PatchogueWeb: /indexaspMin IELTS Band Score: Undergraduate AdmissionsBrigham Y oung University, HawaiiWeb: /Min IELTS Band Score: Undergraduate AdmissionsBrooks College, Long BeachWeb: /index.aspBrooks College, SunnyvaleWeb: /brookssj/index.jspBrooks Institute of PhotographyWeb: /Min IELTS Band Score: Undergraduate and Graduate AdmissionsBroward Community CollegeMin IELTS Band Score:Undergraduate admissionsBrown InstituteWeb: /brown/aboutus.htmlBrown UniversityWeb: /gsMin IELTS Band Score:Graduate admissionsBryn Mawr CollegeWeb: /admissionsMin IELTS Band Score: Undergraduate and Graduate AdmissionsButler UniversityWeb: Min IELTS Band Score: Undergraduate and Graduate AdmissionsCalifornia Culinary Academy - Le Gordon Bleu Hospitality & Restaurant Management Program Web: /California Institute of TechnologyWeb: /Min IELTS Band Score: Undergraduate and Graduate AdmissionsCalifornia Lutheran UniversityWeb: Min IELTS Band Score: Undergraduate and Graduate AdmissionsCalifornia School of Culinary ArtsWeb: /California State University, ChicoWeb: Min IELTS Band Score: Undergraduate AdmissionsCalifornia State University, FullertonWeb: /Min IELTS Band Score:Undergraduate and Graduate admissionsCalifornia State University, HaywardWeb: Min IELTS Band Score: Undergraduate and Graduate AdmissionsCalifornia State University, Long BeachWeb: /Min IELTS Band Score: Undergraduate and Graduate AdmissionsCalifornia State University, Los AngelesWeb: /Min IELTS Band Score: Undergraduate and Graduate AdmissionsCalifornia State University, NorthridgeWeb: /Min IELTS Band Score: Undergraduate and Graduate AdmissionsCalifornia State University, SacramentoWeb: /Min IELTS Band Score:Undergraduate and Graduate admissionsCameron UniversityWeb: Min IELTS Band Score:Undergraduate and Graduate admissionsCarl Albert State College, PoteauWeb: /Carl Albert State College, SallisawWeb: /sequoyah_county/index.htmCarleton CollegeWeb: Min IELTS Band Score: Undergraduate AdmissionsCarnegie MellonWeb: /enrollment/admission/Min IELTS Band Score: Undergraduate AdmissionsCase Western Reserve UniversityWeb: Min IELTS Band Score: Undergraduate AdmissionsCatholic University of AmericaWeb: /Min IELTS Band Score: Undergraduate AdmissionsChandler-Gilbert Community CollegeChatham CollegeWeb: Min IELTS Band Score:Undergraduate admissionsCity University, BellevueWeb: Min IELTS Band Score: Undergraduate and Graduate AdmissionsCity University, RentonWeb: Min IELTS Band Score: Undergraduate and Graduate AdmissionsClaremont Graduate UniversityWeb: Min IELTS Band Score: Graduate AdmissionsClaremont Graduate University, The Peter F. Drucker and Masatoshi Ito Graduate School of ManagementWeb: /pages/1378.aspMin IELTS Band Score: Graduate AdmissionsClarion University of PennsylvaniaWeb: Min IELTS Band Score: Undergraduate and Graduate AdmissionsClarkson UniversityWeb: Min IELTS Band Score: Undergraduate and Graduate admissionsCleveland State UniversityWeb: Min IELTS Band Score: Undergraduate and Graduate AdmissionsCollins CollegeWeb: /index.aspMin IELTS Band Score: Undergraduate AdmissionsColorado School of MinesWeb: /Academic/econbus/Min IELTS Band Score: Graduate AdmissionsColorado State UniversityWeb: /Min IELTS Band Score: Undergraduate and Graduate AdmissionsColumbia University Teachers' CollegeWeb: /academic/appliedlinguistics/default.htmMin IELTS Band Score: Graduate AdmissionsColumbia University, Mastr of Arts Program in Climate and SocietyWeb: /cu/climatesociety/admissions.htmlMin IELTS Band Score: Graduate admissionsColumbia University, School of Arts and SciencesWeb: /cu/gsas/Min IELTS Band Score:Graduate admissionsColumbia University, School of International and Public AffairsWeb: /Min IELTS Band Score:Graduate admissionsCommission on Graduates of Foreign Nursing SchoolsWeb: Min IELTS Band Score:Intl. Commission on Healthcare Professions; Credential Evaluation/Certification ProgramConcordia CollegeWeb: Min IELTS Band Score: Undergraduate AdmissionsConnecticut CollegeWeb: /admissions/visiting/international-info/admissionMin IELTS Band Score: Undergraduate AdmissionsConnors State College, MuskogeeConnors State College, WarnerContra Costa Community CollegeWeb: Min IELTS Band Score: Undergraduate admissionsCooking and Hospitality Institute of ChicagoWeb: /home.aspCornell University, Johnson School of Management (MBA program)Web: Min IELTS Band Score: Graduate admissionsDe Anza CollegeWeb: Min IELTS Band Score: Undergraduate admissionsDeV ry UniversityWeb: Min IELTS Band Score:Undergraduate and Graduate AdmissionsAll 29 CampusesDoane CollegeWeb: /Min IELTS Band Score: Undergraduate and Graduate AdmissionsDowntown College ConsortiumWeb: /Min IELTS Band Score:Undergraduate and graduate admissions for:Oklahoma City Community CollegeOklahoma State University-Oklahoma CityRedlands Community CollegeRose State CollegeUniversity of Central OklahomaDrury UniversityWeb: Min IELTS Band Score: Undergraduate and Graduate AdmissionsDuke UniversityWeb: Min IELTS Band Score:Undergraduate and Graduate admissionsDuncan Higher Education CenterWeb: /duncan/Min IELTS Band Score:Undegraduate and graduate admissions for consortium of:Western Oklahoma State College (2 year college), Southwestern Oklahoma State University, East Central University, University of Sciences and Arts of Oklahoma and Cameron University.East Carolina UniversityWeb: /Min IELTS Band Score: Undergraduate and Graduate AdmissionsEast Central UniversityWeb: /Min IELTS Band Score:Undergraduate and Graduate admissionsEast Stroudsburg UniversityWeb: /servlet/RetrievePage?site=esu&page=home Min IELTS Band Score: Graduate AdmissionsEast Tennessee State UniversityWeb: /gradstudMin IELTS Band Score: Graduate AdmissionsEastern Michigan UniversityMin IELTS Band Score:Undergraduate and Graduate admissions Eastern Oklahoma State College, McAlesterEastern Oklahoma State College, WilburtonEastern Oregon UniversityWeb: /Min IELTS Band Score: Undergraduate AdmissionsEastern Washington UniversityWeb: /Min IELTS Band Score: Undergraduate and Graduate AdmissionsEckerd CollegeWeb: Min IELTS Band Score: Undergraduate AdmissionsEdmonds Community CollegeWeb: /Elon UniversityWeb: Min IELTS Band Score:Undergraduate and Graduate admissionsEmpire State College, State University of New Y orkWeb: /esconline/online2.nsf/eschome?openform Min IELTS Band Score: Undergraduate AdmissionsEstrella Mountain Community CollegeFairleigh Dickinson UniversityWeb: /Min IELTS Band Score: Undergraduate and Graduate AdmissionsFashion Institute of Design and MerchandisingWeb: /Florida Metropolitan UniversityWeb: Min IELTS Band Score: Undegraduate and Graduate admissionsFlorida National CollegeWeb: /Min IELTS Band Score: Undergraduate AdmissionsFoothill CollegeWeb: Min IELTS Band Score:Undergraduate admissionsFordham UniversityWeb: /prospective/Admissions/Graduate5142html Min IELTS Band Score: Graduate AdmissionsFranklin and Marshall CollegeWeb: /Min IELTS Band Score:Undergraduate admissionsFull Sail Real World EducationWeb: Min IELTS Band Score:Undergraduate admissionsGateway Community CollegeWeb: /Gemological Institute of AmericaWeb: /wd_3954htmMin IELTS Band Score: Undergraduate AdmissionsGeneva CollegeWeb: /Min IELTS Band Score: Undergraduate AdmissionsGeorge Mason UniversityWeb: Min IELTS Band Score: Undergraduate and Graduate AdmissionsGeorge Washington UniversityWeb: /Min IELTS Band Score: Undergraduate and Graduate AdmissionsGeorgetown UniversityWeb: Min IELTS Band Score: Graduate admissionsGeorgia Institute of TechnologyWeb: /Min IELTS Band Score: Graduate AdmissionsGibbs College, MontclairWeb: /kgsnj/index.htmlGibbs College, NorwalkWeb: /kgsct/index.jspGibbs School, Washington DCWeb: /kgsdc/index.jspGlendale Community CollegeWeb: /index.htmlGlendale Community College, Maricopa CountyWeb: /Golden Gate UniversityWeb: Min IELTS Band Score: Undergraduate and Graduate AdmissionsGoldey-Beacom CollegeWeb: Min IELTS Band Score: Undergraduate and Graduate AdmissionsGraduate Institute of Applied LinguisticsWeb: Min IELTS Band Score: Graduate AdmissionsGrand V alley State UniversityWeb: /Min IELTS Band Score: Undergraduate and Graduate AdmissionsGreen River Community CollegeWeb: /Hamilton CollegeWeb: /Min IELTS Band Score: Undergraduate AdmissionsHamline UniversityWeb: /Harrington Institute of Interior DesignWeb: /Min IELTS Band Score:Undergraduate admissionsHarvard Business School (Doctoral Programs)Web: /doctoralMin IELTS Band Score: Graduate AdmissionsHarvard Business School MBA ProgramsWeb: Min IELTS Band Score:Graduate admissionsHarvard University, School of Public HealthWeb: /admissionsMin IELTS Band Score:Graduate admissionsHaverford CollegeWeb: /admissionMin IELTS Band Score: Undergraduate AdmissionsHawaii Pacific UniversityWeb: /Min IELTS Band Score: Undergraduate and Graduate AdmissionsHusson CollegeWeb: /Min IELTS Band Score: Undergraduate AdmissionsIdaho State UniversityWeb: /Min IELTS Band Score: Undergraduate and Graduate AdmissionsIllinois Institute of Art at Chicago-Schaumburg, TheWeb: /about_us_the_region.aspIndiana Institute of TechnologyWeb: /Min IELTS Band Score: Undergraduate and Graduate AdmissionsIndiana State UniversityWeb: /Min IELTS Band Score: Graduate AdmissionsIndiana UniversityWeb: /~iuadmit/graduate/index.shtmlMin IELTS Band Score: Graduate AdmissionsIndiana University Purdue UniversityWeb: /Min IELTS Band Score: Undergraduate AdmissionsIndiana University, School of LawWeb: /Min IELTS Band Score:Graduate admissionsIndiana University, School of MusicWeb: /Min IELTS Band Score: Undergraduate AdmissionsInternational Academy of Design and Technology, Chicago Web: /Min IELTS Band Score: Undergraduate AdmissionsInternational Academy of Design and Technology, DetroitMin IELTS Band Score: Undergraduate AdmissionsInternational Academy of Design and Technology, Fairmont Web: /Min IELTS Band Score: Undergraduate AdmissionsInternational Academy of Design and Technology, Orlando Web: /Min IELTS Band Score: Undergraduate AdmissionsInternational Academy of Design and Technology, PittsburghWeb: /Min IELTS Band Score: Undergraduate AdmissionsInternational Academy of Design and Technology, TampaWeb: /Min IELTS Band Score: Undergraduate AdmissionsInternational Commission on Healthcare Professions (ICHP)Web: International Culinary AcademyWeb: /ica/index.htmlInternational Monetary Fund (IMF) Institute, Washington DCWeb: Min IELTS Band Score: /external/np/ins/english/pdf/inst2004.pdfIowa State UniversityWeb: /Min IELTS Band Score: Undergraduate and Graduate AdmissionsJacksonville UniversityWeb: Min IELTS Band Score: Undergraduate and Graduate AdmissionsJohns Hopkins University, School of Advanced International StudiesWeb: http://www.jhubc.it/Min IELTS Band Score:Graduate admissionsJohnson and Wales University, DenverWeb: /denver/index.htmMin IELTS Band Score: Undergraduate and Graduate AdmissionsJohnson and Wales University, North MiamiWeb: /florida/index.htmMin IELTS Band Score: Undergraduate and Graduate AdmissionsJohnson and Wales University, ProvidenceWeb: /prov/index.htmMin IELTS Band Score: Undergraduate and Graduate AdmissionsKatharine Gibbs School, BostonWeb: /Katharine Gibbs School, MelvilleWeb: /index.aspKatharine Gibbs School, New Y orkWeb: /Katharine Gibbs School, PhiladelphiaWeb: /Katharine Gibbs School, PiscatawayWeb: /Katharine Gibbs School, ProvidenceWeb: /kgri/index.jspKentucky Board Of NursingWeb: /index-old.htmKirksville College of Osteopathic MedicineWeb: /Min IELTS Band Score:Graduate admissionsKnox CollegeWeb: /knox/Min IELTS Band Score: Undergraduate AdmissionsLane Community CollegeWeb: /Langston UniversityWeb: /Min IELTS Band Score:Undergraduate and graduate admissionsLangston University, Oklahoma CityWeb: /okcweb/Min IELTS Band Score:Undergraduate and graduate admissionsLangston University, TulsaWeb: /tulsa/index.htmMin IELTS Band Score:Undegraduate and graduate admissions Lawrence Technological UniversityMin IELTS Band Score:Graduate admissionsLawton East Central UniversityWeb: Min IELTS Band Score:Undergraduate and Graduate admissionsLe Cordon Bleu College of Culinary Arts, AtlantaWeb: /atlantac/index.jspLe Cordon Bleu College of Culinary Arts, Brown InstituteWeb: /Le Cordon Bleu College of Culinary Arts, Las V egasWeb: /vegasc/index.jspLehigh UniversityWeb: Min IELTS Band Score: Undergraduate AdmissionsLewis and Clark CollegeWeb: /Min IELTS Band Score: Undergraduate AdmissionsLinfield CollegeWeb: /Min IELTS Band Score: Undergraduate AdmissionsLong Island UniversityWeb: /cwis/cwp/but02/applying/app_instruct.html Min IELTS Band Score: Undergraduate and graduate admissionsLoyola University Chicago, English as a Second Language Program Web: /depts/esl/adreq.htmlMin IELTS Band Score:Graduate admissionsLynn UniversityWeb: Min IELTS Band Score:Undergraduate and Graduate AdmissionsMadonna UniversityWeb: Min IELTS Band Score: Undergraduate and Graduate AdmissionsManhattan School of MusicWeb: /Min IELTS Band Score: Undergraduate and Graduate AdmissionsMarist UniversityWeb: /gce/graduate/internationalMin IELTS Band Score: Graduate admissionsMarquette UniversityWeb: /Min IELTS Band Score:UndergraduateMarshall UniversityWeb: Min IELTS Band Score:Undergraduate and Graduate admissionsMary Baldwin CollegeWeb: Min IELTS Band Score: Undergraduate and Graduate AdmissionsMarylhurst UniversityWeb: /Min IELTS Band Score: Undergraduate and Graduate AdmissionsMarymount College of Fordham UniversityWeb: /Min IELTS Band Score: Undergraduate AdmissionsMaryville CollegeWeb: /Min IELTS Band Score: Undergraduate AdmissionsMcCurtain County HIgher Educaiton CenterWeb: /Min IELTS Band Score:Undegraduate and graduate admissions for consortium of:Carl Albert State College (Associate Degree), Eastern Oklahoma University (Associate Degree), Southeastern Oklahoma State University (undegraduate and graduate admissions).McIntosh CollegeWeb: /mcintosh/index.jspMesa Community CollegeMethodist Theological School in OhioWeb: /Min IELTS Band Score: Undergraduate and Graduate AdmissionsMetropolitan State College of DenverWeb: /Min IELTS Band Score: UndergraduateMiami UniversityWeb: /Min IELTS Band Score: Undergraduate and Graduate AdmissionsMichigan State UniversityWeb: /Min IELTS Band Score:Undergraduate admissionsMiddlebury CollegeWeb: /Min IELTS Band Score: Undergraduate AdmissionsMilwaukee School of EngineeringWeb: /Min IELTS Band Score:Undergraduate and graduate admissions Missouri CollegeMonroe CollegeWeb: /Min IELTS Band Score: Undergraduate AdmissionsMontana State UniversityWeb: /Min IELTS Band Score:Undergraduate and graduate admissionsMonterey Institute of International StudiesWeb: /Min IELTS Band Score: Graduate AdmissionsMoreno V alley Community CollegeWeb: http://209.129.6.218:9000/Mount Holyoke CollegeWeb: Min IELTS Band Score: Undergraduate AdmissionsMountain State UniversityWeb: Min IELTS Band Score: Undergraduate and Graduate admissionsMurray State CollegeWeb: /National UniversityWeb: Min IELTS Band Score:Undergraduate and graduate admissionsNebraska Wesleyan UniversityWeb: /Min IELTS Band Score: Undergraduate AdmissionsNew Mexico Highlands UniversityWeb: /Min IELTS Band Score:Undergraduate and graduate admissionsNew Y ork Restaurant SchoolWeb: /New Y ork UniversityWeb: /Min IELTS Band Score:Undergraduate admissionsNew Y ork University's Tisch School of the ArtsWeb: /tisch/Min IELTS Band Score:Undergraduate and graduate admissionsNorco Community CollegeWeb: /~norco/North Dakota State UniversityWeb: Min IELTS Band Score:Undergraduate admissions Northeastern Oklahoma A&M CollegeNortheastern State UniversityWeb: /Min IELTS Band Score:Undergraduate and Graduate admissionsNortheastern University, Graduate School of Arts and Sciences Web: /graduateMin IELTS Band Score: Graduate AdmissionsNorthern Illinois UniversityWeb: /index.htmlMin IELTS Band Score:Undergraduate admissionsNorthern Kentucky UniversityWeb: /Min IELTS Band Score:Undergraduate and graduate admissions Northern Oklahoma College, EnidNorthern Oklahoma College, TonkawaNorthWest Arkansas Community CollegeWeb: /Min IELTS Band Score: Undergraduate admissionsNorthwest Baptist SeminaryWeb: /home.htmMin IELTS Band Score: Graduate AdmissionsNorthwestern Oklahoma State UniversityWeb: Min IELTS Band Score:Undergraduate and Graduate admissionsNorthwestern UniversityMin IELTS Band Score: Graduate AdmissionsNorthwestern University, Kellogg School of ManagementWeb: /Min IELTS Band Score:Graduate admissionsNorthwood UniversityWeb: /scr/index.aspMin IELTS Band Score:Undergraduate and graduate admissionsNotre Dame CollegeWeb: /admissions/international2.htm Min IELTS Band Score:Undergraduate and graduate admissionsNova Southeastern UniversityWeb: /cwis/registrar/isssMin IELTS Band Score: Undergraduate and graduate admissionsOhio Wesleyan UniversityWeb: Min IELTS Band Score: Undergraduate AdmissionsOklahoma City Community CollegeOklahoma City UniversityWeb: /Min IELTS Band Score:Undergraduate and graduate admissionsOklahoma Panhandle State UniversityMin IELTS Band Score:Undergraduate and graduate admissionsOklahoma State Board of RegentsWeb: /Min IELTS Band Score:Undergraduate and Graduate admissions - All campuses statewideOklahoma State UniversityWeb: /Min IELTS Band Score:Undergraduate and Graduate admissionsOklahoma State University, College of Osteopathic MedicineWeb: /college/index.htmMin IELTS Band Score:Graduate admissionsOklahoma State University, Tech. Branch, Oklahoma CityWeb: /home/Min IELTS Band Score:Undergraduate and graduate admissionsOklahoma State University, Tech. Branch, OkmulgeeWeb: /Min IELTS Band Score:Undergraduate and graduate admissionsOklahoma State University, TulsaWeb: /Min IELTS Band Score:Undergraduate and graduate admissionsOld Dominion UniversityWeb: /Min IELTS Band Score:Undergraduate and graduate admissionsWeb: /Min IELTS Band Score:Undergraduate and graduate admissionsOrlando Culinary AcademyParadise V alley Community CollegePennsylvania Culinary InstitutePennsylvania State UniversityWeb: /Min IELTS Band Score: Undergraduate AdmissionsPepperdine UniversityWeb: Min IELTS Band Score: Undergraduate AdmissionsPepperdine University, Graziadio School of Business and Management Web: /Min IELTS Band Score: Graduate AdmissionsPepperdine University, School of Public PolicyWeb: Min IELTS Band Score:Graduate admissionsPhoenix CollegePittsburg State UniversityWeb: Min IELTS Band Score:Undergraduate admissionsPitzer CollegeWeb: Min IELTS Band Score:Undergraduate admissions - CPE exams onlyPlymouth State UniversityWeb: Min IELTS Band Score:Undergraduate admissionsPoint Loma Nazarene UniversityWeb: /Min IELTS Band Score: Undergraduate and graduate admissionsWeb: /Min IELTS Band Score: Undergraduate and graduate admissionsPrinceton UniversityWeb: Min IELTS Band Score:Graduate admissionsPurdue UniversityWeb: /Min IELTS Band Score: Undergraduate admissionsQuinnipiac UniversityWeb: /Min IELTS Band Score: Undergraduate and graduate admissions Redlands Community CollegeReformed Bible CollegeWeb: Min IELTS Band Score: Undergraduate admissionsRensselaer Polytechnic InstituteWeb: /Min IELTS Band Score:Graduate admissionsRice UniversityWeb: /Grad/Admissions/Min IELTS Band Score: Graduate AdmissionsRio Salado CollegeRiverside Community CollegeWeb: /citycampus/index.htmRoanoke CollegeWeb: Min IELTS Band Score:Undergraduate admissionsRogers State UniversityWeb: /Min IELTS Band Score:Undergraduate and graduate admissions Rogers State University, BartlesvilleWeb: /bville/Min IELTS Band Score:Undergraduate admissionsRogers State University, PryorWeb: /pryor/Min IELTS Band Score:Undergraduate admissionsRutgers University, CamdenWeb: /Min IELTS Band Score:Graduate admissionsRutgers University, New Brunswick/PiscatawayWeb: /Min IELTS Band Score: Graduate admissionsRutgers University, NewarkWeb: /Min IELTS Band Score: Graduate admissionsSaint Mary's College of CaliforniaWeb: Min IELTS Band Score:Undergraduate admissionsSaint Michael CollegeWeb: Min IELTS Band Score: Graduate admissionsSalisbury UniversityWeb: Min IELTS Band Score:Undergraduate and graduate admissionsSalve Regina UniversityWeb: /Min IELTS Band Score: Undergraduate and graduate admissionsSan Jose State UniversityWeb: /Min IELTS Band Score: Graduate admissionsSanta Clara University School of LawWeb: /law/Min IELTS Band Score: Graduate admissionsSavannah College of Art and DesignWeb: /Min IELTS Band Score: Udergraduate and graduate admissionsSchiller International UniversityWeb: :8082/siu_tws/USA.html Min IELTS Band Score: Undergraduate and graduate admissionsSchool for International Training (SIT)Web: /degree.htmlMin IELTS Band Score: Graduate admissionsSchool of the Art Institute of ChicagoWeb: /saic/Min IELTS Band Score: Undergraduate and graduate admissionsSchool of the Museum of Fine Arts, BostonWeb: /Min IELTS Band Score:Undergraduate and graduate admissionsSchool of V isual ArtsWeb: Min IELTS Band Score: Undergraduate and graduate admissions Scottsdale Community CollegeScottsdale Culinary InstituteWeb: /Seattle Central Community CollegeWeb: /international/Seattle UniversityWeb: /Min IELTS Band Score: Undergraduate and graduate admissions Seminole State CollegeSlippery Rock UniversityWeb: Min IELTS Band Score:Undergraduate admissionsSouth Mountain Community CollegeSoutheast Missouri State University。
英国利物浦大学基本概况
利物浦大学成立于1881年，是英国著名精英大学联盟“罗素集团大学”成员之一，目前世界排名第173位。
下面是整理并翻译的利物浦大学基本概况，供大家参考。
一、关于利物浦大学The University of Liverpool is one of the UK’s leading researchinstitutions with an annual turnover of ￡465 million, including ￡89 million for research. Ranked in the top 1% of higher education institutions worldwide,Liverpool is a member of the prestigious Russell Group of the UK’s leadingresearch universities. The University has 31,000 students, 7,700 of whom travel from all over the world to study here, and 195,000 alumni in 200 countries. Its global focus has led the institution to establish a university in Suzhou nearShanghai, in partnership with Xi’an Jiaotong University, as well as a campus in London. The University is the largest provider of 100% online postgraduatedegree courses in Europe with over 10,000 students studying for Liverpooldegrees around the world.利物浦大学是英国顶尖的研究大学之一。
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利物浦约翰摩尔斯大学起源于1823年成立的利物浦技术文学院，最初为小规模的工学院。
经过数百年的成长融合，合并不同的院校，在1970年成为利物浦理工学院。
立思辰留学360介绍，它最终在1992年英国政府颁布的继续和高等教育学法案的推动下升格为大学，并以利物浦约翰摩尔大学作为新的名字。
它也是英国北方大学联盟的创造成员之一。
ISC
利物浦约翰摩尔大学ISC (全称：利物浦约翰摩尔大学国际学习中心，简称：LJMU ISC) 是利物浦约翰摩尔大学（LJMU）直属的国际学习中心，专为希望就读LJMU的国际学生提供预科课程，帮助他们升读大学学位课程。
本科预科课程 International Foundation Year
本科预科课程是国际学生直接升读LJMU大学学位课程的捷径，该课程为期三个学期，教学内容包括英语、学术和学习技能等多方面；课程与诸多热门本科专业衔接，为攻读大学学位课程打下坚实基础。
课程商务、工程、计
方向法律与
社会研
究算机与生命科学
课程
长度
3个学期
学术要求完成高二或高三，且成绩优秀，或同等水平
英语
要求
雅思4.5（写作4.0）
入学
时间
每年1月或9月。