joint venture

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Joint resummation for heavy quark production

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We present the joint threshold and recoil resummed transverse momentum distributions for heavy quark hadroproduction, at next-to-leading logarithmic accuracy. We exhibit their dependence on the production channel and the color configurations, and compare t

5

002 tcO 21 1v9410150/hp-pe:hviXraFebruary2,20084:14WSPC/INSTRUCTIONFILEjr

InternationalJournalofModernPhysicsA

cWorldScienti cPublishingCompanyJOINTRESUMMATIONFORHEAVYQUARKPRODUCTION

ANDREABANFI

CavendishLaboratory,UniversityofCambridge

MadingleyRoad,CB30HECambridge,UK

ERICL

A joint model of production scheduling and predictive

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ORIGINAL ARTICLE

A joint model of production scheduling and predictive maintenance for minimizing job tardiness

Ershun Pan &Wenzhu Liao &Lifeng Xi

Received:15July 2010/Accepted:15September 2011/Published online:4October 2011#Springer-Verlag London Limited 2011

Abstract As increasingly diverse tasks are being processed on single multi-functional machine,production scheduling has become a critical issue in the planning and management of manufacturing processes.However,the majority of produc-tion scheduling literature ignores mach

Does Venture Capital Spur Innovation文献翻译

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风险投资能否刺激自主创新

Samuel Kortum and Josh Lerner 写于1998年11月

决策者常常认为风险投资对于自主创新有非常大的影响,然而这种前提并没有被系统的评估。我们用通过检验美国30年通过20种产业专利发明的风险投资的影响来解决这些遗漏。我们用几种方法去解决这种因果关系,包括利用一个1979年的政策转变激发风险投资筹款这种问题。我们发现一个企业的风险投资活动量很大程度上增加了其专利率。然而近些年来对于研发的风险投资率平均低于3%,我们的评估暗示出风险投资占企业自主创新的15%。当一个530家风险投资支持公司和非风险投资支持公司的例子中利用其他创新措施时,我们要解决通过演示相似模型的一个我们利用专利间隔数假象的问题。 1.前言

全球的政府都已经迫切地想复制快速发展的美国的风险投资行业。这些努力都基于一个基本的原理——风险投资已经激发了美国的创新,当然也能激发其他国家。(例如,欧洲委员会的《关于创新的绿皮书》)。

一些传闻支持风险投资刺激了自主创新这种主张。我们来看一个例子,以色列政府在1991年首推两个项目区鼓励风险投资基金的形成。

Strategic Japanese-Chinese Joint Research Program

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FY2012

Strategic Japanese-Chinese Joint Research Program

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Japanese Research Team Leader’s Information

(CV and a list of publications (maximum 5 publications) in the last 5 years, specifying publications relevant to the project)

Chinese Research Team Leader’s Information

(CV and a list of publications (maximum 5 publications) in the last 5 years, specifying publications relevant to the project)

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*Add as many tables as number of Groups

*Add as ma

Clothing Co-parsing by Joint Image Segmentation and Labeling

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计算机视觉

2014 IEEE Conference on Computer Vision and Pattern Recognition

ClothingCo-ParsingbyJointImageSegmentationandLabeling

WeiYang1,PingLuo2,LiangLin1,3 1

SunYat-senUniversity,Guangzhou,China

2

DepartmentofInformationEngineering,TheChineseUniversityofHongKong3

SYSU-CMUShundeInternationalJointResearchInstitute,Shunde,China

{platero.yang,pluo.lhi}@http://www.77cn.com.cn,linliang@http://www.77cn.com.cn

Abstract

Thispaperaimsatdevelopinganintegratedsystemofclothingco-parsing,inordertojointlyparseasetofcloth-ingimages(unsegmentedbutannot

Joint Temporal Density Measurements for Two-Photon State Characterization

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We demonstrate a new technique for characterizing two-photon quantum states based on joint temporal correlation measurements using time resolved single photon detection by femtosecond upconversion. We measure for the first time the joint temporal density o

JointTemporalDensityMeasurementsforTwo-PhotonStateCharacterization

OnurKuzucu,1FrancoN.C.Wong,1SunaoKurimura,2andSergeyTovstonog2

1

ResearchLaboratoryofElectronics,MassachusettsInstituteofTechnology,Cambridge,Massachusetts02139,USA

2

NationalInstituteforMaterialsScience,1-1Nami

Joint Temporal Density Measurements for Two-Photon State Characterization

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We demonstrate a new technique for characterizing two-photon quantum states based on joint temporal correlation measurements using time resolved single photon detection by femtosecond upconversion. We measure for the first time the joint temporal density o

JointTemporalDensityMeasurementsforTwo-PhotonStateCharacterization

OnurKuzucu,1FrancoN.C.Wong,1SunaoKurimura,2andSergeyTovstonog2

1

ResearchLaboratoryofElectronics,MassachusettsInstituteofTechnology,Cambridge,Massachusetts02139,USA

2

NationalInstituteforMaterialsScience,1-1Nami

A COLOR-BASED PARTICLE FILTER FOR JOINT DETECTION AND TRACKING OF MULTIPLE OBJECTS

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Recent works have shown that the particle filter using color as observation feature is a powerful technique for tracking deformable objects in image sequences with complex backgrounds. This paper presents a hybrid valued sequential state estimation algorit

ACOLOR-BASEDPARTICLEFILTERFORJOINTDETECTIONANDTRACKINGOF

MULTIPLEOBJECTS

JacekCzyza,BrankoRisticbandBeno tMacqa

(a)

UCL-TELE,BatimentSt´evin,PlaceduLevant2,1348Louvain-la-Neuve,Belgium

(b)

DSTO,ISRD200labs,POBox1500,EdinburghSA5111,Australia

ABSTRACT

Recentworkshaveshownthat

Joint generalization of city points and road network for small-scale mapping

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城市点与路网集成的小比例尺制图综合

Joint generalization of city points and road network for

small-scale mapping

1*T. E. Samsonov1,2, A. M. Krivosheina1 Lomonosov Moscow State University, Faculty of Geography, 1 Leninskiye Gory, Moscow, Russia, 119234

*Email: tsamsonov@geogr.msu.ru

2Delaunay Laboratory of Discrete and Computational Geometry, Yaroslavl State University,

150000, Sovetskaya Str. 14, Yaroslavl, Russian Federation

1. Introduction

Multiscale cartographic web services such as Google Maps, Microsoft Bing and OpenStreetMap are produced usin

Joint generalization of city points and road network for small-scale mapping

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城市点与路网集成的小比例尺制图综合

Joint generalization of city points and road network for

small-scale mapping

1*T. E. Samsonov1,2, A. M. Krivosheina1 Lomonosov Moscow State University, Faculty of Geography, 1 Leninskiye Gory, Moscow, Russia, 119234

*Email: tsamsonov@geogr.msu.ru

2Delaunay Laboratory of Discrete and Computational Geometry, Yaroslavl State University,

150000, Sovetskaya Str. 14, Yaroslavl, Russian Federation

1. Introduction

Multiscale cartographic web services such as Google Maps, Microsoft Bing and OpenStreetMap are produced usin