Dissertation_proposals_AP
国自然标书proposal 后缀
国自然标书proposal 后缀摘要:一、国自然标书proposal 简介1.国自然标书proposal 的概念2.国自然标书proposal 的重要性二、国自然标书proposal 的后缀要求1.后缀的定义与作用2.后缀的分类及要求2.1 文件类型后缀2.2 文件内容后缀2.3 文件提交时间后缀三、国自然标书proposal 后缀的常见问题与解答1.文件类型后缀问题2.文件内容后缀问题3.文件提交时间后缀问题四、国自然标书proposal 后缀的注意事项1.确保后缀的合规性2.及时更新后缀要求3.避免因后缀问题影响申请正文:国自然标书proposal 是申请国家自然科学基金项目的关键文件,对于科研工作者来说具有重要意义。
一份成功的国自然标书proposal 不仅要有严谨的研究内容,还需要符合相关后缀要求。
本文将详细介绍国自然标书proposal 的后缀要求及注意事项。
首先,我们需要了解国自然标书proposal 的后缀是什么。
后缀,也称为文件扩展名,是用来表示文件类型的一种标识。
在国自然标书proposal 中,后缀用于区分文件的类型、内容和提交时间等。
因此,正确使用后缀对于申请国家自然科学基金项目至关重要。
接下来,我们来了解国自然标书proposal 的后缀要求。
根据国家自然科学基金委员会(NSFC)的规定,国自然标书proposal 的后缀应符合以下要求:1.文件类型后缀:通常为国自然标书proposal 的文件类型为.docx 或.pdf。
请确保文件格式正确,以免影响评审。
2.文件内容后缀:国自然标书proposal 的内容应包括:项目名称、申请人信息、申报单位信息、研究背景与意义、研究目标与内容、研究方法与技术路线、可行性分析、预期成果与创新点、研究计划与进度安排、经费预算等内容。
请确保所提交的proposal 文件包含了所有必要的内容,且内容准确无误。
3.文件提交时间后缀:国自然标书proposal 的提交时间有明确的规定,通常分为上、下半年两个批次。
2023 华为 Datacom-HCIE 真题题库
2023 华为Datacom-HCIE 真题题库单项选择题1.[试题编号:190585] (单选题)华为SD-WAN解决方案中,当CPE位于NAT设备后的私网时,特别是两个站点的CPE同时位于NAT设备后的私网时,CPE之间需要使用NAT穿越技术。
华为SD-WAN解决方案中使用以下哪一项技术帮助CPE之间实现NAT穿越?A、NAT ALGB、NAT serverC、IPsec VPND、STUN答案:D解析:华为SD-WAN解决方案是一种通过网络控制器集中管理CPE设备、零配置开局的解决方案,可以帮助企业应对云服务带来的挑战,做到业务随需而变。
当CPE 位于NAT设备后的私网时,特别是两个站点的CPE同时位于NAT设备后的私网时,CPE之间需要使用NAT穿越技术,才能实现业务流量的互通。
华为SD-WAN 解决方案中使用STUN技术帮助CPE之间实现NAT穿越。
下面我来分析一下各个选项:A项:NAT ALG。
这个描述是错误的,因为NAT ALG是一种应用层网关技术,用于修改应用层报文中的IP地址和端口信息,以适应NAT转换后的地址变化,而不是用于实现NAT穿越。
B项:NAT server。
这个描述也是错误的,因为NAT server是一种NAT设备上的功能,用于将公网IP地址和端口映射到私网IP地址和端口,以提供对外服务,而不是用于实现NAT穿越。
C项:IPsec VPN。
这个描述同样是错误的,因为IPsec VPN是一种安全隧道技术,用于在不安全的网络中建立加密和认证的通道,以保护数据传输的安全性,而不是用于实现NAT穿越。
D项:STUN。
这个描述是正确的,因为STUN是一种NAT会话穿越应用程序,用于检测网络中是否存在NAT设备,并获取两个通信端点经NAT设备分配的IP 地址和端口号,在两个通信端点之间建立一条可穿越NAT的P2P链接2。
2.[试题编号:190584] (单选题)如图所示,在虚拟化园区网络中部署业务随行,其中PC1属于Sales安全组,PC2属于R&D安全组,PC3属于Market安全组。
【计算机应用研究】_选择策略_期刊发文热词逐年推荐_20140725
科研热词 遗传算法 特征选择 无线传感器网络 对等网络 链式 连接度 近似全局最少优先 边界网关协议 资源共享 贪婪路由 能量采集 罚函数 网关选择 统一描述、发现和集成 约束优化 空间信息网格 移动自组网 神经网络 热土豆路由 洪泛 机构名识别 服务质量路由 服务质量 智能体 旅行商问题 文本分类 文件块选择 数据库 数据优先级 支持向量机 平面文件 局部最少优先 局部最优化问题 存储粒度 存储策略 存储模型 多约束服务质量路由 多准则 域间出口选择 地理位置 列车脱轨 信息网格 传输延时 仿真 主动学习 web服务 native-xml med欺骗 k最近邻 internet互连
无线自组织网 无人机 旅行商问题 数据包标记 故障诊断 性能比较 心电检测算法 微粒群算法 并行景象匹配 对等计算 多目标遗传算法 多样性 多参数优化 多传感器协同 启发式信息 可测试性设计 变采样间隔 历史导航信息 协同通信 协同训练 半监督学习 十字路口节点 加权hausdorff距离 功率分配 剪枝 划分测试 分布式系统 分布式拒绝服务攻击 决策支持 冗余检测器 内容分发 克隆规模 克隆扩增 偏好区域 候选节点集选择策略 信道分配 信息熵 传感器节点 仿真 任务调度 人工鱼群算法 人工免疫算法 交互多模型滤波器 互相关 中继选择 中继 中文组织机构名 丢包率比例区分 不等差错保护 web服务 two-way中继 turbo码 tri-training pbest
dissertation高分句式 -回复
dissertation高分句式-回复以下是一些可以使用的高分句式:1. "通过综合分析,我们可以得出结论..."(By conducting a comprehensive analysis, we can come to the conclusion that...)2. "在当前研究领域中尚未有关于此问题的充分研究,因此本文旨在填补这一研究空白"(There is currently a lack of sufficient research on this issue in the field, thus the purpose of this dissertation is to fill this research gap.)3. "本研究采用了多种有效的研究方法和技术,以确保数据的准确性和可靠性"(This study employs various effective research methods and techniques to ensure the accuracy and reliability of the data.)4. "通过对比不同数据来源的结果,我们可以得出一致的结论"(By contrasting the results from different data sources, we can draw consistent conclusions.)5. "本文的研究为进一步深入理解该问题提供了重要的理论和实践价值" (This dissertation provides important theoretical and practical value for further understanding of the issue.)6. "通过深入挖掘案例研究,我们能够发现新的见解和观点"(Through in-depth exploration of case studies, we are able to discover new insights and perspectives.)7. "在充分考虑了限制和偏差的情况下,我们可以对研究结果的可靠性和有效性具有高度的信心"(With thorough consideration oflimitations and biases, we can have high confidence in the reliability and validity of the research findings.)8. "本文的研究结果为相关领域的决策制定者提供了有价值的参考和指导"(The research findings of this dissertation provide valuable references and guidance for decision-makers in relevant fields.)9. "未来的研究可以进一步探索该问题的不同方面和潜在影响"(Future research can further explore different aspects and potential impacts of this issue.)10. "本研究的贡献在于提供了一种新的分析框架和方法,以解决现有研究中存在的局限性"(The contribution of this study lies in providing a new analytical framework and method to address the limitations of existing research.)。
apset 评分算法 -回复
apset 评分算法-回复APSET评分算法是一种用于学术成就评估的工具,可以帮助评委们对申请人的学术能力进行定量评估。
它基于主题模型和机器学习算法,能够从大量的学术论文中提取出关键信息,并量化成绩用以评估。
APSET评分算法的基本原理是通过分析学术论文的内容和结构,识别出其中的主题,并对论文与主题之间的关系进行建模。
这个过程可以分为以下几个步骤:1. 数据预处理:在评估之前,论文需要经过一系列的预处理步骤,如去除停用词、标点符号和数字。
此外,还需要进行词干化和词形还原等操作,以便更好地理解论文内容。
2. 主题模型训练:在数据预处理完成后,需要使用主题模型对论文进行训练。
主题模型是一种通过对文档进行潜在主题分配的概率模型,常用的主题模型有Latent Dirichlet Allocation (LDA)和Latent Semantic Analysis (LSA)等。
这些模型可以从学术论文中挖掘出潜在的主题,为后续的评分提供基础。
3. 特征提取:在主题模型训练完成后,需要对论文进行特征提取。
常用的特征包括词频、TF-IDF和主题分布等。
通过这些特征,可以量化每篇论文的学术能力,并为评分打下基础。
4. 评分模型训练:在特征提取完成后,可以使用机器学习算法训练评分模型。
常用的算法包括支持向量机、神经网络和随机森林等。
评分模型可以通过学习已有的论文与对应分数之间的关系,预测评分结果。
5. 评分结果生成:在评分模型训练完成后,可以使用该模型对待评估的论文进行评分。
评分结果通常是一个连续值,表示该论文的学术能力水平。
根据评分结果,可以对申请人进行排序,选择最具潜力的候选人进行进一步的考察。
值得注意的是,APSET评分算法虽然可以帮助评委们更全面客观地评估学术能力,但它仅作为一个参考工具,并不能完全取代人工评估。
最终的选拔结果应该结合评委们的专业判断和实地考察等综合因素进行综合评价。
综上所述,APSET评分算法是一种通过主题模型和机器学习算法对学术论文进行评估的工具。
意见的英文单词
意见的英文单词【篇一:表建议、命令的英语单词用法】suggestvt.1. 建议,提议[+v-ing][+(that)][+wh-]i suggest our going to the park on sunday.我建议我们星期天去公园。
the dentist suggested that she牙医建议她改天再来。
2. 暗示;启发[+(that)]her expressionsuggested pleasure.她面露喜色。
3. 使人想起,使人联想到[(+to)]that cloud suggests a boat to me.那朵云使我联想到船。
advisevt.1. 劝告,忠告[o][o2][o5][o6]we advised her that she (should) wait.我们劝她等。
we advised him against acting in haste.我们劝他不要匆忙行事。
2. 当...的顾问[w][(+on)]3. 通知,告知[(+of)][o5][o6]please advise us of any change in your plan.你们的计划倘有变更,请告诉我们。
(should) come another day.4. 建议采取proper和建议没什么大关系啊...应该是 propose1. 提议,建议,提出[+v-ing][+(that)]it was proposed we go to the station to meet our guests. 有人建议我们去车站接客人。
he proposed building a bridge across this river.他建议在这条河上造一座桥。
he proposed a get-together this weekend.他建议本周末聚会。
2. 提(名),推荐i proposed mr. hunter for the job.我提议亨特先生来干这工作。
Proposal
Research and Design of the 3D Reconstruction System Based on Binocular Stereo VisionMajor: civil engineeringSchool of civil engineeringChongqing UniversityNovember, 2017Opening report1.The topic of this research:How to make artificial intelligence (automobile, robot) aware of our world is a complex problem. The 3D reconstruction based on stereo vision provides us a direction. Stereoscopic vision of three dimensional reconstruction means the way to restore the geometry of 3D visible surfaces from two or more two-dimensional images in computer vision. Stereo vision is a simple, reliable, flexible and widely used method to simulate human eyes' processing of scenery. In the computer stereo vision system, two images of the same scene can be obtained from different angles by using a camera. Then, the 3D shape of the scene is reconstructed by computer, and the spatial position information of the object is recovered.2.The purpose of this researchThe purpose of this research is to make computer have the ability of 3d environmental information, using digital camera as image sensor, using image processing, visual computing and other technologies for non-contact 3d measurement, using computer program to obtain 3d information of objects, and 3d model reconstruction, which will not only enable the machine to perceive the geometric information of objects in a three-dimensional environment, including its shape, position, posture, motion, etc., and can describe, store, identify and understand them.3. The research significance of the thesisHuman beings acquire information from the external environment, mainly through the eyes. There is a saying called "seeing is believing", is to illustrate the importance of the information obtained by human eyes. According to scientists statistics, most of the human perception of the world, about 60% from the visual, auditory information accounted for 20%, the other, such as taste, tactile information and so on add up to 20%. And human beings get visual information from the outside world, and use the brain to judge, processing is a very complex process. In the real world, any three-dimensional object has a very complex structure and color information, this information through the retina in order to convert the two-dimensional image information, the information transmitted in the brain pathways of photoreceptor cells, the brain for the final 3D reconstruction and color and position determination. The human brain is an extremely complex and developed processing system that can quickly process this information so that humans can quickly identify objects in the external environment.The purpose of computer vision system is to recognize the 3D world through the projection of the 3D scene on the camera plane. With the rapid development of computer hardware and software facilities and technology, computer vision theoryhas also been rapid developed.Allowing computers to recognize objects as fast as humans has been a relentless pursuit of human beings and decades of dreams. It is the main work of computer vision to use the computer to replace the human visual information in the environment of the outside world.4.Research methods (or experiment)To conduct this research, we will try to figure out some questions first.1.The argumentation of the subject, that is, the purpose of the subject and thebackground of project.2.The innovation point of the subject research. For example, is it new to most of us?Or is it possible that this idea will be widely used after most of us know about it?After solving these problems, the main research methods of this topic are as follows:(1)Document analysis(2)Combination of empirical analysis and logical analysisThe methods used in this study are:Monocular vision,Binocularvision,Trinocular vision5. Anticipated results3D reconstruction based on stereo matching and camera calibration, using SFM (Structure from Motion) algorithm to restore external camera parameters, and then calculate the 3D coordinates of discrete space points, triangulation and texture after Delaunay, finally through the OpenGL programming display 3D model.6. Details of the experimentOur research decides to adopt SFM which belongs to monocular vision method.SFM (Structure from motion) is a method to use numerical method to recover camera parameters and 3D information through detecting matching feature points in multiple images which is not calibrated. SFM requires very low image, and can be reconstructed by video or even random sequence of images. At the same time, the image sequence can be used to realize the camera self-calibration in the process of reconstruction, which eliminates the steps of camera calibration in advance. And because of the progress of feature point extraction and matching technology, the robustness of the SFM is also very strong. Another advantage of the SFM is that it can reconstruct large scale scenes, and the number of input images can reach millions. It is very suitable for 3D reconstruction of natural terrain and urban landscape. It is flexible and convenient to use. It is suitable for all kinds of complicated occasions with less cost. So it is the most widely used method.7.References[1]HORN B.Shape from shading: a method for obtaining the shape of a smooth opaque object from one view[D].Cambridge: [s.n.],1970.[2]BELHUMEUR P,KRIEGMAN D,YUILLE A.The bas-relief ambiguity [J].International Journal of Computer Vision,1999,35 ( 1 ) :33-44.[3]BAKSHI S,YANG Y.Shape from shading for non-lambertian surfaces [C]/ /Proc of International Conference on Image Processing.1994:130-134.[4]PENNA M.A shape from shading analysis for a single perspective image ofa polyhedro[J].IEEE Trans on Pattern Analysis and Machine Intelligence,1989,11( 6) : 545-554.[5]VOGEL O,BREUB M,WEICKERT J.Perspective shape from shading with non-lambertian reflectance[C]/ /Proc of DAGM Symposium on Pattern Recognition.Berlin: Springer,2008: 517-526.[6]ECKER A,JEPSON A D.Polynomial shape from shading[C]/ /Proc of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2010.[7]WOODHAM R J.Photometric method for determining surface orientation from multiple images[J].Optical Engineering,1980,19 ( 1)139-144.[8]NOAKES L,KOZERA R.Nonlinearities and noise reduction in 3- source photometric stereo[J].Journal of Mathematical Imaging and Vision,2003,18( 2) : 119-127.[9]HOROVITZ I,KIRYATI N.Depth from gradient fields and control points: bias correction in photometric stereo[J].Image and Vision Computing,2004,22( 9) : 681-694.[10]TANG L K,TANG C K,WANG T T.Dense photometric stereo using tensorial belief propagation[C]/ /Proc of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.San Diego:[s.n.],2005: 132-139.。
Thermo-calc软件-TCCP用户指南 (有目录索引)
Thermo-Calc®User’s GuideVersion PThermo-Calc Software ABStockholm Technology ParkBjörnnäsvägen 21SE-113 47 Stockholm, SwedenCopyright © 1995-2003 Foundation of Computational ThermodynamicsStockholm, Sweden目录第1部分一般介绍 (12)1.1 计算热力学 (12)1.2 Thermo-Calc软件/数据库/界面包 (12)1.3 致谢 (13)1.4 版本历史 (13)1.5 Thermo-Calc软件包的通用结构 (13)1.6 各类硬件上Thermo-Calc软件包的有效性 (14)1.7 使用Thermo-Calc软件包的好处 (14)第2部分如何成为Thermo-Calc专家 (14)2.1 如何容易地使用本用户指南 (14)2.2 如何安装和维护Thermo-Calc软件包 (16)2.2.1 许可要求 (16)2.2.2 安装程序 (16)2.2.3 维护当前和以前版本 (16)2.2.4 使TCC执行更方便 (16)2.3 如何成为Thermo-Calc专家 (16)2.3.1 从TCSAB与其世界各地的代理获得迅速技术支持 (17)2.3.2 日常使用各种Thermo-Calc功能 (17)2.3.3 以专业的和高质量的标准提交结果 (17)2.3.4 通过各种渠道相互交换经验 (17)第3部分Thermo-Calc软件系统 (17)3.1 Thermo-Calc软件系统的目标 (17)3.2 一些热力学术语的介绍 (18)3.2.1 热力学 (18)3.2.2 体系、组元、相、组成、物种(System, component, phases, constituents and species) (18)3.2.3 结构、亚点阵和位置 (19)3.2.4 成分、构成、位置分数、摩尔分数和浓度(composition, constitution, site fractions, molefractions and concentration) (19)3.2.5 平衡态和状态变量 (19)3.2.6 导出变量 (22)3.2.7 Gibbs相规则 (25)3.2.8 状态的热力学函数 (25)3.2.9 具有多相的体系 (25)3.2.10 不可逆热力学 (26)3.2.11 热力学模型 (26)3.2.12 与各种状态变量有关的Gibbs能 (27)3.2.13 参考态与标准态 (27)3.2.14 溶解度范围 (28)3.2.15 驱动力 (28)3.2.16 化学反应 (28)3.2.17 与平衡常数方法相对的Gibbs能最小化技术 (28)3.2.18 平衡计算 (29)3.3 热力学数据 (30)3.3.1 数据结构 (30)3.3.3 数据估价 (32)3.3.6 数据加密 (33)3.4 用户界面 (34)3.4.1 普通结构 (34)3.4.2 缩写 (34)3.4.3 过程机制(history mechanism) (35)3.4.4 工作目录和目标目录(Working directory and target directory) (35)3.4.5 参数转换为命令 (36)3.4.6 缺省值 (36)3.4.7 不理解的问题 (36)3.4.8 帮助与信息 (36)3.4.9 出错消息 (36)3.4.10 控制符 (36)3.4.11 私人文件 (36)3.4.12 宏工具 (37)3.4.13 模块性 (37)3.5 Thermo-Calc中的模块 (37)3.5.1 基本模块 (37)3.7 Thermo-Calc编程界面 (39)3.7.1 Thermo-Calc作为引肇 (39)3.7.2 Thermo-Calc应用编程界面:TQ和TCAPI (40)3.7.3 在其它软件包中开发Thermo-Calc工具箱 (43)3.7.4 材料性质计算核材料工艺模拟的应用 (43)3.8 Thermo-Calc的功能 (44)3.9 Thermo-Calc应用 (44)第4部分Thermo-Calc数据库描述 (45)4.1 引言 (45)4.2 Thermo-Calc数据库描述形式 (45)第5部分数据库模块(TDB)——用户指南 (55)5.1 引言 (55)5.2 TDB模块中用户界面 (56)5.3 开始 (56)5.3.1 SWITCH-DATABASE (56)5.3.2 LIST-DATABASE ELEMENT (56)5.3.3 DEFINE_ELEMENTS (56)5.3.4 LIST_SYSTEM CONSTITUENT (56)5.3.5 REJECT PHASE (56)5.3.6 RESTORE PHASE (56)5.3.7 GET_DATA (56)5.4 所有TDB监视命令的描述 (56)5.4.1 AMEND_SELACTION (56)5.4.6 DEFINE_SPECIES (58)5.4.7 DEFINE_SYSTEM (58)5.4.8 EXCLUDE_UNUSED_SPECIES (58)5.4.9 EXIT (58)5.4.10 GET_DATA (58)5.4.11 GOTO_MODULE (59)5.4.12 HELP (59)5.4.13 INFORMA TION (59)5.4.14 LIST_DATABASE (60)5.4.15 LIST_SYSTEM (60)5.4.16 MERGE_WITH_DA TABASES (61)5.4.17 NEW_DIRECTORY_FILE (61)5.4.18 REJECT (61)5.4.19 RESTORE (62)5.4.20 SET_AUTO_APPEND_DA TABASE (62)5.4.21 SWITCH_DA TABASE (63)5.5 扩展命令 (64)第6部分数据库模块(TDB)——管理指南 (64)6.1 引言 (64)6.2 TDB模块的初始化 (65)6.3 数据库定义文件语法 (66)6.3.1 ELEMENT (67)6.3.2 SPECIES (67)6.3.3 PHASE (67)6.3.4 CONSTITUENT (67)6.3.5 ADD_CONSTITUENT (68)6.3.6 COMPOUND_PHASE (68)6.3.7 ALLOTROPIC_PHASE (68)6.3.8 TEMPERA TURE_LIMITS (68)6.3.9 DEFINE_SYSTEM_DEFAULT (69)6.3.10 DEFAULT_COMMAND (69)6.3.11 DATABASE_INFORMATION (69)6.3.12 TYPE_DEFINITION (69)6.3.13 FTP_FILE (70)6.3.14 FUNCTION (70)6.3.15 PARAMETER (72)6.3.16 OPTIONS (73)6.3.17 TABLE (73)6.3.18 ASSESSED_SYSTEMS (73)6.3.19 REFERENCE_FILE (74)6.3.20 LIST_OF_REFERENCE (75)6.3.21 CASE与ENDCASE (76)6.3.22 VERSION_DA TA (76)6.5 数据库定义文件实例 (77)6.5.1 例1:一个小的钢数据库 (77)6.5.2 例2:Sb-Sn系个人数据库 (78)第7部分制表模块(TAB) (81)7.1 引言 (81)7.2 一般命令 (81)7.2.1 HELP (81)7.2.2 GOTO_MODULE (81)7.2.3 BACK (82)7.2.4 EXIT (82)7.2.5 PATCH (82)7.3 重要命令 (82)7.3.1 TABULATE_SUBSTANCE (82)7.3.2 TABULATE_REACTION (85)7.3.3 ENTER_REACTION (86)7.3.4 SWITCH_DA TABASE (87)7.3.5 ENTER_FUNCTION (88)7.3.6 TABULATE_DERIV A TIVES (89)7.3.7 LIST_SUBSTANCE (91)7.4 其它命令 (92)7.4.1 SET_ENERGY_UNIT (92)7.4.2 SET_PLOT_FORMAT (92)7.4.3 MACRO_FILE_OPEN (92)7.4.4 SET_INTERACTIVE (93)7.5 绘制表 (93)第8部分平衡计算模块(POL Y) (94)8.1 引言 (94)8.2 开始 (95)8.3 基本热力学 (95)8.3.1 体系与相 (95)8.3.2 组元(Species) (95)8.3.3 状态变量 (96)8.3.4 组分 (97)8.3.5 条件 (98)8.4 不同类型的计算 (98)8.4.1 计算单一平衡 (98)8.4.2 性质图的Steping计算 (99)8.4.3 凝固路径模拟 (99)8.4.4 仲平衡与T0温度模拟 (99)8.4.5 相图的Mapping计算 (101)8.4.6 势图计算 (101)8.4.7 Pourbaix图计算 (101)8.4.8 绘制图 (101)8.5.4 更高阶相图 (104)8.5.5 性质图 (104)8.6 普通命令 (104)8.6.1 HELP (104)8.6.2 INFORMA TION (104)8.6.3 GOTO_MODULE (105)8.6.4 BACK (105)8.6.5 SET_INTERACTIVE (105)8.6.6 EXIT (106)8.7 基本命令 (106)8.7.1 SET_CONDITION (106)8.7.2 RESET_CONDITION (107)8.7.3 LIST_CONDITIONS (107)8.7.4 COMPUTE_EQUILIBRIUM (107)8.7.6 DEFINE_MATERIAL (108)8.7.6 DEFINE_DIAGRAM (111)8.8 保存和读取POL Y数据结构的命令 (112)8.8.1 SA VE_WORKSPACES (112)8.8.2 READ_WORKSPACES (113)8.9 计算与绘图命令 (114)8.9.1 SET_AXIS_V ARIABLE (114)8.9.2 LIST_AXIS_V ARIABLE (114)8.9.3 MAP (114)8.9.4 STEP_WITH_OPTIONS (115)8.9.5 ADD_INITIAL_EQUILIBRIUM (117)8.9.6 POST (118)8.10 其它有帮助的命令 (118)8.10.1 CHANGE_STA TUS (118)8.10.2 LIST_STA TUS (119)8.10.3 COMPUTE_TRANSITION (120)8.10.4 SET_ALL_START_V ALUES (121)8.10.5 SHOW_V ALUE (122)8.10.6 SET_INPUT_AMOUNTS (122)8.10.7 SET_REFERENCE_STA TE (122)8.10.8 ENTER_SYMBOL (123)8.10.9 LIST_SYMBOLS (124)8.10.10 EV ALUATE_FUNCTIONS (124)8.10.11 TABULATE (124)8.11 高级命令 (125)8.11.1 AMEND_STORED_EQUILIBRIA (125)8.11.3 DELETE_INITIAL_EQUILIBRIUM (126)8.11.4 LIST_INITIAL_EQUILIBRIA (126)8.11.5 LOAD_INITIAL_EQUILIBRIUM (126)8.11.10 SELECT_EQUILIBRIUM (128)8.11.11 SET_NUMERICAL_LIMITS (128)8.11.12 SET_START_CONSTITUTION (129)8.11.13 SET_START_V ALUE (129)8.11.14 PATCH (129)8.11.15 RECOVER_START_V ALUE (129)8.11.16 SPECIAL_OPTIONS (129)8.12 水溶液 (132)8.13 排除故障 (133)8.13.1 第一步 (133)8.13.2 第二步 (133)8.13.3 第三步 (133)8.14 频繁提问的问题 (134)8.14.1 程序中为什么只得到半行? (134)8.14.2 在已经保存之后为什么不能绘图? (134)8.14.3 为什么G.T不总是与-S相同? (134)8.14.4 如何获得组元偏焓 (135)8.14.5 为什么H(LIQUID) 是零而HM(LIQUID)不是零 (135)8.14.6 即使石墨是稳定的为什么碳活度小于1? (135)8.14.7 如何获得过剩Gibbs能? (135)8.14.8 当得到交叉结线而不是混溶裂隙时什么是错的? (135)8.14.9 怎么能直接计算最大混溶裂隙? (136)第9部分后处理模块(POST) (136)9.1 引言 (136)9.2 一般命令 (137)9.2.1 HELP (137)9.2.2 BACK (137)9.2.3 EXIT (137)9.3 重要命令 (137)9.3.1 SET_DIAGRAM_AXIS (137)9.3.2 SET_DIAGRAM_TYPE (138)9.3.3 SET_LABEL_CORVE_OPTION (139)9.3.5 MODIFY_LABEL_TEXT (139)9.3.6 SET_PLOT_FORMAT (140)9.3.7 PLOT_DIAGRAM (141)9.3.8 PRINT_DIAGRAM (142)9.3.9 DUMP_DIAGRAM (143)9.3.10 SET_SCALING_STA TUS (144)9.3.11 SET_TITLE (144)9.3.12 LIST_PLOT_SETTINGS (144)9.4 实验数据文件绘图命令 (144)9.4.1 APPEND_EXPERIMENTAL_DA TA (144)9.4.2 MAKE_EXPERIMENTAL_DA TAFILE (145)9.5.3 SET_AXIS_LENGTH (147)9.5.4 SET_AXIS_TEXT_STATUS (147)9.5.5 SET_AXIS_TYPE (147)9.5.6 SET_COLOR (147)9.5.7 SET_CORNER_TEXT (148)9.5.8 SET_FONT (148)9.5.9 SET_INTERACTIVE_MODE (149)9.5.10 SET_PLOT_OPTION (149)9.5.11 SET_PREFIX_SCALING (149)9.5.12 SET_REFERENCE_STA TE (149)9.5.13 SET_TIELINE_STA TE (150)9.5.14 SET_TRUE_MANUAL_SCALING (150)9.5.15 TABULATE (150)9.6 奇特的命令 (150)9.6.1 PATCH_WORKSPACE (150)9.6.2 RESTORE_PHASE_IN_PLOT (150)9.6.3 REINIATE_PLOT_SETTINGS (151)9.6.4 SET_AXIS_PLOT_STATUS (151)9.6.5 SET_PLOT_SIZE (151)9.6.6 SET_RASTER_STATUS (151)9.6.8 SUSPEND_PHASE_IN_PLOT (151)9.7 3D图标是:命令与演示 (151)9.7.1 CREATE_3D_PLOTFILE (153)9.7.2 在Cortona VRML Client阅读器中查看3D图 (154)第10部分一些特殊模块 (155)10.1 引言 (155)10.2 特殊模块生成或使用的文件 (156)10.2.1 POL Y3文件 (156)10.2.2 RCT文件 (156)10.2.3 GES5文件 (156)10.2.4 宏文件 (157)10.3 与特殊模块的交互 (157)10.4 BIN模块 (157)10.4.1 BIN模块的描述 (157)10.4.2 特定BIN模块数据库的结构 (161)10.4.3特定BIN计算的演示实例 (162)10.5 TERN 模块 (162)10.5.1 TERN 模块的描述 (162)10.5.2 特殊TERN模块数据库的结构 (166)10.5.3 TERN模块计算的演示实例 (167)10.6 POT模块 (167)10.7 POURBAIX 模块 (167)10.8 SCHAIL 模块 (167)11.2 热化学 (168)11.2.1 一些术语的定义 (168)11.2.2 元素与物种(Elements and species) (168)11.2.3 大小写模式 (169)11.2.4 相 (169)11.2.5 温度与压力的函数 (169)11.2.6 符号 (170)11.2.7 混溶裂隙 (170)11.3 热力学模型 (170)11.3.1 标准Gibbs能 (171)11.3.2 理想置换模型 (171)11.3.3 规则溶体模型 (171)11.3.4 使用组元而不是元素 (172)11.3.5 亚点阵模型—化合物能量公式 (172)11.3.6 离子液体模型,对具有有序化趋势的液体 (172)11.3.7 缔合模型 (173)11.3.8 准化学模型 (173)11.3.9 对Gibbs能的非化学贡献(如铁磁) (173)11.3.10 既有有序-无序转变的相 (173)11.3.11 CVM方法:关于有序/无序现象 (173)11.3.12 Birch-Murnaghan模型:关于高压贡献 (173)11.3.13 理想气体模型相对非理想气体/气体混合物模型 (173)11.3.14 DHLL和SIT模型:关于稀水溶液 (173)11.3.15 HKF和PITZ模型:对浓水溶液 (173)11.3.16 Flory-Huggins模型:对聚合物 (173)11.4 热力学参数 (173)11.5 数据结构 (175)11.5.1 构造 (175)11.5.2 Gibbs能参考表面 (175)11.5.3 过剩Gibbs能 (175)11.5.4 存储私有文件 (175)11.5.5 加密与不加密数据库 (176)11.6 GES系统的应用程序 (176)11.7 用户界面 (176)11.7.1 模块性和交互性 (177)11.7.2 控制符的使用 (177)11.8 帮助与信息的命令 (177)11.8.1 HELP (177)11.8.2 INFORMATION (177)11.9 改变模块与终止程序命令 (178)11.9.1 GOTO_MODULE (178)11.9.2 BACK (178)11.9.3 EXIT (178)11.10 输入数据命令 (178)11.10.4 ENTER_SYMBOL (180)11.10.5 ENTER_PARAMETER (181)11.11 列出数据的命令 (183)11.11.1 LIST_DATA (183)11.11.2 LIST_PHASE_DA TA (183)11.11.3 LIST_PARAMETER (184)11.11.4 LIST_SYMBOL (185)11.11.5 LIST_CONSTITUENT (185)11.11.6 LIST_STATUS (185)11.12 修改数据命令 (185)11.12.1 AMEND_ELEMENT_DA TA (185)11.12.2 AMEND_PHASE_DESCRIPTION (186)11.12.3 AMEND_SYMBOL (188)11.12.4 AMEND_PARAMETER (189)11.12.5 CHANGE_STATUS (191)11.12.6 PATCH_WORKSPACES (191)11.12.7 SET_R_AND_P_NORM (191)11.13 删除数据的命令 (192)11.13.1 REINITIATE (192)11.13.2 DELETE (192)11.14 存储或读取数据的命令 (192)11.14.1 SA VE_GES_WORKSPACE (192)11.14.2 READ_GES_WORKSPACE (193)11.15 其它命令 (193)11.15.1 SET_INTERACTIVE (193)第12部分优化模块(PARROT) (193)12.1 引言 (193)12.1.1 热力学数据库 (194)12.1.2 优化方法 (194)1 2.1.4 其它优化软件 (195)12.2 开始 (195)12.2.1 试验数据文件:POP文件 (195)12.2.2 图形试验文件:EXP文件 (197)12.2.3 系统定义文件:SETUP文件 (197)12.2.4 工作文件或存储文件:PAR文件 (198)12.2.5 各种文件名与其关系 (198)12.2.6 交互运行PARROT模块 (199)12.2.6.3 绘制中间结果 (199)12.2.6.4 实验数据的选择 (199)12.2.6.6 优化与连续优化 (200)12.2.7 参数修整 (200)12.2.8 交互完成的变化要求编译 (201)12.3 交替模式 (201)12.4 诀窍与处理 (201)12.4.4 参数量 (201)12.5 命令结构 (201)12.5.1 一些项的定义 (201)12.5.2 与其它模块连接的命令 (201)12.5.3 用户界面 (201)12.6 一般命令 (201)12.7 最频繁使用的命令 (202)12.8 其它命令 (203)第13部分编辑-实验模块(ED-EXP) (203)第14部分系统实用模块(SYS) (203)14.1 引言 (203)14.2 一般命令 (203)14.2.1 HELP (203)14.2.2 INFORMA TION (204)14.2.4 BACK (205)14.2.5 EXIT (205)14.2.6 SET_LOG_FILE (205)14.2.7 MACRO+FILE_OPEN (205)14.2.8 SET_PLOT_ENVIRONMENT (206)14.3 Odd命令 (207)14.3.1 SET_INTERACTIVE_MODE (207)14.3.2 SET_COMMAND_UNITS (207)14.3.4 LIST_FREE_WORKSPACE (207)14.3.5 PATCH (207)14.3.6 TRACE (207)14.3.7 STOP_ON_ERROR (208)14.3.8 OPEN_FILE (208)14.3.9 CLOSE_FILE (208)14.3.10 SET_TERMINAL (208)14.3.11 NEWS (208)14.3.12 HP_CALCULATOR (208)14.4 一般信息的显示 (209)第15部分数据绘图语言(DATAPLOT) (215)第1部分一般介绍1.1 计算热力学在近十年内与材料科学与工程相联系的计算机计算与模拟的研究与发展已经为定量设计各种材料产生了革命性的方法,热力学与动力学模型的广泛结合使预测材料成分、各种加工后的结构和性能。
一种改进的否定选择算法
12 卜连续 位 匹配 规则 .
在 人 工 免 疫 系 统 中 应 用 最 多 的就 是 rb匹 配 规 则 。与 海 c
明距离 匹配相 比,c 匹配可 以更好 的体现两个字符 串间的相 rb 似程度 。 c rb匹配规则考虑 的是 连续位的匹配情况 , 即两个字 符 串连续 匹配的最大位数 。在 rb匹配 规则 的应用 中, c 参数 r 的选择是一个很棘手 的问题 ; 此外 ,c rb匹配脱离了整体 , 只是 比较其 中某 一 串连续 的字符段 ,所 以 rb匹配在某些情况下 c 也存在着缺 陷 。会将好 的候选检 测器丢弃 , 而保留下 了不恰 当的候选 检测器 , 进而会导致在检测 阶段 的误报、 漏报 。例:
摘要 : 以否定选择 算法为基础 , 分别对海 明距 离匹配与 r _ 字符块 匹配规则进行改进并结合使用 , 而产生一种新 的匹配 从 规 则。通过最后的 实验证 明, 基于双重 匹配规则的否定选择算 法产 生 了更加 高效的检 测器 , 并且提 高了系统的检测率 ,
减 少 了误 报 。
( )X 0 0 0 11 1 2 1 00 l 1 1
Y l 1O 1 1 01 0】 1 Ol
现的: 事先确定一个 阈值 , 果两个字符 串的匹配度超过阈值, 如 则认为它们 匹配 。否 定选择算 法中应用 比较广泛 的匹配规则 是海 明距离 匹配和 r 续位 匹配( cniu u i ,c ) 则, - 连  ̄ o t o s t rb 规 n bs 另外 r _ 字符块 匹配 (- u k , h 规 则也经常会被提到 。通过 r h n sr ) c c 否定选择 ,生成 了系统所 需要 的成熟检测器 。应用不 同的匹
字符段都含有各 自特定 的意义 ,c rb匹配没有对两个字符 串中
国外留学proposal写作技巧及注意事项
The GAP What will be new?
However, while much has been written in English, and in detail, about the zones in China’s coastal area (e.g. SFERT, 2005), focused on export and with massive foreign direct investment (e.g. SMERT, 2005), inland developments in China have not been studied.
Investigation of Industrial Zones
Get it right
The Written Proposal The first page
You need the “Big Picture”
There is the big “Big Picture” And the small “Big Picture”
Now
The big “Big Picture” In this case: developing economies
They were an immediate success in China and the concept is spreading rapidly among other developing nations in Asia such as India (Nallathiga, 2007) and Vietnam (Vietnam Agency, 2007). (2008) gives a detailed overview of the growth of such zones.
proposal-based method -回复
proposal-based method -回复[Proposal-Based Method]Title: Implementing a Proposal-Based Method for Effective Decision MakingIntroduction (200 words)Effective decision making is crucial for the success of any individual or organization. It involves careful evaluation of available options and selection of the best course of action. One method that has gained popularity in recent years is the proposal-based method. This method focuses on generating and analyzing proposals or alternatives before making a final decision. In this article, we will delve into the proposal-based method, discussing its key components, advantages, and steps involved in its implementation.I. Understanding the Proposal-Based Method (300 words)A. Definition and Purpose: The proposal-based method involves generating multiple proposals or alternatives, and evaluating them against predefined criteria or objectives to make an informed decision.B. Key Components: The main components of this method includeproposal generation, evaluation, ranking, and selection.C. Advantages of Proposal-Based Method: This method ensures a comprehensive evaluation of options, encourages creativity, improves decision quality, promotes participation, and reduces bias.II. Steps to Implement Proposal-Based Method (600 words)A. Identify Decision-Making Criteria: Clearly define the objectives and criteria that the proposals should address. This step ensures that all proposals are evaluated consistently.B. Generate Proposals: Encourage brainstorming sessions to generate a wide variety of proposals. Each proposal should be unique and aligned with the decision-making criteria established in step A.C. Evaluate Proposals: Analyze each proposal against the defined decision-making criteria. Use techniques like decision matrices, cost-benefit analysis, and SWOT analysis to assess the strengths and weaknesses of each proposal objectively.D. Rank Proposals: Assign weights to the decision-making criteria and rank each proposal based on its performance. This step helps identify the most promising alternatives and provides a basis for further evaluation.E. Select Proposal: Based on the ranking, select the proposal that best aligns with the decision-making criteria and has the highest overall score. Consider additional factors like feasibility, cost, and potential risks before making the final decision.F. Implement and Monitor: Once a proposal is selected, develop an action plan and implement it. Regularly monitor and evaluate progress to ensure the desired outcomes are achieved.III. Case Study: Applying Proposal-Based Method in a Tech Startup (400 words)To illustrate the effectiveness of the proposal-based method, let us consider a case study of a tech startup developing a new product. The company wants to choose the best technology stack for their product development. By implementing the proposal-based method, they:- Identified decision-making criteria such as scalability, performance, and cost-effectiveness.- Generated proposals, including using a popular stack, using a cutting-edge but expensive stack, and developing a custom stack. - Evaluated each proposal using criteria-specific metrics and techniques like prototyping, cost analysis, and expert opinions.- Ranked the proposals based on their performance against thedefined criteria, with the custom stack receiving the highest ranking.- Selected the custom stack, considering its flexibility,cost-effectiveness, and alignment with their long-term goals.- Implemented the selected stack, constantly monitoring and adjusting its usage as the project progressed.Conclusion (200 words)The proposal-based method offers an effective approach to decision making by considering multiple alternatives before finalizing a course of action. By following a systematic process, organizations can ensure thoughtful evaluation, increased participation, reduced bias, and improved decision quality. While this method requires time and effort, it provides a holistic view of available options, leading to better outcomes. Implementing the proposal-based method can greatly enhance decision-making processes across various industries and improve an organization's overall success.。
strawman proposal例子
strawman proposal例子
"Strawman proposal" 通常用来描述一个初步的提案,它可能只是一个初始的想法或建议,需要在后续的讨论中进一步细化和完善。
这个术语经常在技术、开发、标准制定等领域中使用。
以下是一个简单的"Strawman proposal" 的例子,假设我们正在讨论某个软件项目中的新功能:
Strawman Proposal: 添加评论功能
问题描述:
我们的应用目前缺乏用户之间的互动,用户无法对内容进行评论。
为了增加用户参与度和社交性,我们考虑添加评论功能。
初步建议:
1. 在每个帖子下面添加一个评论框,允许用户输入评论。
2. 将评论保存在数据库中,与相应的帖子关联。
3. 提供用户界面,使用户可以查看和回复其他用户的评论。
4. 实现简单的点赞和回复功能,以促进互动。
可能的问题和考虑:
1. 需要考虑用户隐私和安全问题,如何防范恶意评论?
2. 如何处理大量评论,以及在性能上的优化?
3. 是否考虑添加匿名评论的功能?
4. 需要与现有系统和用户界面进行良好的集成。
下一步行动:
1. 在团队会议中讨论这个初步建议,收集成员的反馈。
2. 考虑是否需要进行用户调查,以确定用户是否有兴趣和需求。
3. 逐步完善这个提案,包括详细的技术实现方案、界面设计等。
这只是一个简单的例子,实际上"Strawman proposal" 的内容和形式会根据具体的项目和领域而有所不同。
这样的提案通常是为了启动初步的讨论,吸引团队成员提出更具体和全面的想法。
propagation策略
Propagation策略是Spring框架中事务管理的一个关键属性,它决定了事务如何在不同的方法之间传播。
具体来说,Propagation策略定义了当一个事务方法被另一个事务方法调用时,应该如何使用事务。
以下是Spring中定义的几种Propagation策略:
1.PROPAGATION_REQUIRED:这是最常见的选择。
如果当前存在事务,那么就加入到这个事务中。
如果当前没有事务,就新建一个事务。
2.PROPAGATION_SUPPORTS:支持当前事务,如果当前没有事务,就以非事务方式执行。
3.PROPAGATION_MANDATORY:支持当前事务,如果当前没有事务,就抛出异常。
4.PROPAGATION_REQUIRES_NEW:新建事务,如果当前存在事务,把当前事务挂起。
5.PROPAGATION_NOT_SUPPORTED:以非事务方式执行操作,如果当前存在事务,就把当前事务挂起。
6.PROPAGATION_NEVER:以非事务方式执行,如果当前存在事务,则抛出异常。
7.PROPAGATION_NESTED:如果当前存在事务,则在嵌套事务内执行。
如果当前没有事务,则进行与PROPAGATION_REQUIRED类似的
操作。
以上就是Spring中定义的所有Propagation策略。
这些策略可以帮助你更灵活地管理你的事务,以满足不同的业务需求。
set_ideal_network -no_propagation用法 -回复
set_ideal_network -no_propagation用法-回复set_ideal_network 是一个用于设置网络的理想传播模式的函数。
该函数可以用于优化和调整网络节点之间的连接方式,以实现更高效、更可靠的数据传输和通信。
传统的网络传播模式存在一些缺陷和局限性,例如信号衰减、干扰、延迟等问题,这些问题可能导致数据传输的错误和不稳定性。
为了解决这些问题,研究人员提出了一种新的传播模式——no_propagation(无传播)。
no_propagation 模式的基本原理是消除传播过程中产生的影响和干扰,使数据能够直接从发送节点到达接收节点,从而提高数据传输的效率和成功率。
下面将详细介绍set_ideal_network 函数的用法,并讨论它在不同领域的应用。
首先,我们需要了解set_ideal_network 函数的基本调用方法。
该函数接受一个参数,即网络对象的引用,通过修改网络对象的属性来设置传播模式。
以下是函数的调用示例:pythonset_ideal_network(network)接下来,我们将一步一步地回答set_ideal_network 函数的用法以及它在不同领域的应用。
一、理论基础no_propagation 模式的原理基于无线通信中的近场传输原理。
传统的无线通信模式中,信号会通过空气或其他介质的传播而衰减、受到干扰,从而导致数据传输的不可靠性。
而no_propagation 模式通过直接将信号传递到接收节点,避免了传播过程中的干扰,从而提高了数据传输的效率和可靠性。
二、函数参数说明set_ideal_network 函数需要一个网络对象的引用作为参数。
网络对象是一个数据结构,用于表示网络中的节点和它们之间的连接关系。
通过修改网络对象的属性,我们可以对网络的连接方式进行调整。
三、函数实现步骤set_ideal_network 函数的实现步骤如下:1. 创建一个空的理想网络对象。
stable_diffusion_prompt更改提示词权重原理__概述及解释说明
stable diffusion prompt更改提示词权重原理概述及解释说明1. 引言1.1 概述本文旨在探讨稳定扩散模型中更改提示词权重的原理,并对其进行解释和说明。
稳定扩散模型是一种常用的模型,在自然语言处理、机器学习等领域中有着广泛的应用。
而提示词权重的调整则可以提高模型性能,使得生成的结果更加准确和可靠。
因此,研究和理解如何修改提示词权重的影响因素对于进一步优化稳定扩散模型具有重要意义。
1.2 文章结构本文将从以下几个方面展开论述:首先,介绍stable diffusion prompt更改提示词权重原理的背景和概述;其次,阐明提示词权重在该原理中的重要性;随后,详细探讨修改提示词权重可能产生的影响因素;最后,通过数值实验来验证并解读这些理论。
1.3 目的本文的目标是清晰地描述stable diffusion prompt更改提示词权重原理以及其在稳定扩散模型中的作用。
通过对这一主题的深入分析和解释,我们希望能够增加人们对该原理的理解,并为进一步研究和应用提供有价值的参考。
笔者准备从背景概述、重要性和影响因素三个方面详细论述stable diffusion prompt更改提示词权重原理。
同时,还将通过数值实验来验证这一原理,并对结果进行解读。
最后,本文将对所得结论进行总结,并提出未来研究的建议。
2. stable diffusion prompt更改提示词权重原理:2.1 原理概述在NLP模型中,prompt工程是指为了优化生成结果而对输入进行调整的一种方法。
其中,stable diffusion prompt是一种有效的提示建设技术,可以通过调整生成过程中不同单词或短语的权重来指导模型生成更准确、有逻辑性的结果。
2.2 提示词权重的重要性在NLP任务中,给定一个输入序列作为提示信息,模型需要根据这些提示来合理地生成输出结果。
而不同单词或短语在提示信息中的位置和权重往往对生成结果产生显著影响。
LanLuo-UniversityofSouthernCalifornia
Lan Luo Array Marshall School of BusinessUniversity of Southern California3660 Trousdale Parkway, ACC 306Los Angeles, CA 90089Academic Positions2014 – present: Associate Professor of Marketing (with tenure)University of Southern California2014 – present: Associate Academic Director, the Center for Global InnovationMarshall School of Business, University of Southern California2005 – 2014: Assistant Professor of MarketingUniversity of Southern CaliforniaEducationPh.D. in Business (Marketing Major), University of Maryland, 2005Dissertation: Essays of New Product DevelopmentM.A. (Economics), State University of New York at Buffalo, 2002B.S. (Information Systems), Nankai University, China, 1997Honors and Awards2015, ISMS Doctoral Dissertation Competition Award (Co-mentor of Ph.D. Student Courtney Paulson) 2012, AMA Advanced Research Techniques (ART) Forum Best Paper Award2011, MSI Young Scholar, Marketing Science Institute, awarded once every two years to scholars most likely to be “potential leaders of the next generation of Marketing academics”2010, Dean’s Research Excellence Award, awarded to 4 of over 120 USC Marshall research faculty 2009, Donald R. Lehmann Award, the best dissertation-based paper published in Journal of Marketing or Journal of Marketing Research in the last two years2009, Paul E. Green Award (finalist), the article published in Journal of Marketing Research that demonstrates the most potential to contribute significantly to the practice of marketing research 2008, John D.C. Little Award, the best paper published in Marketing Science or the marketing section of Management Science2007, Marshall Golden Apple Award for Teaching Excellence, presented every year to the professors who have had the greatest impact on their students, as determined by the members of thegraduating class2006, University of Houston Doctoral Symposium Faculty Fellow2005, Marvin A. Jolson Outstanding Marketing Doctoral Student Award, University of Maryland 2004, Society for Marketing Advances Best Doctoral Dissertation Proposal Award2003 – 2004, INFORMS Marketing Science Doctoral Consortium FellowRefereed Journal Publications1. Lan Luo and Jiong Sun (2016), “New Product Design under Channel Acceptance: Brick-and-Mortar, Online Exclusive, or Brick-and-Click”, forthcoming, Production and OperationsManagement.2. Dongling Huang and Lan Luo (2016), “Consumer Preference Elicitation of Complex Productsusing Fuzzy Support Vector Machine Active Learning,” Marketing Science, Special Issue: “Big Data”, Vol. 35, No. 3.*AMA Advanced Research Techniques (ART) Forum Best Paper Award3. Lan Luo and Olivier Toubia (2015), “Improving Online Idea Generation Platforms andCustomizing Task Structure on the Basis of Consumer’s Domain Specific Knowledge,”Journal of Marketing, Vol. 79, No. 5, 100-114.4. Lan Luo, Brian T. Ratchford, and Botao Yang (2013), “Why We Do What We do: A Model ofActivity Consumption,”Journal of Marketing Research, Vol. 50, No. 1, 24-43.5. Lan Luo (2011), “Product Line Design for Consumer Durables: An Integrated Marketing andEngineering Approach,”Journal of Marketing Research, Vol.48, No.1, 128-139.6. Lan Luo, Jack (Xinlei) Chen, Jeanie Han, and C. W. Park (2010), “Dilution and Enhancement ofCelebrity Brands through Sequential Movie Releases,”Journal of Marketing Research,Vol.47, No.6, 1114-1128.7. Lan Luo, P. K. Kannan, and Brian T. Ratchford (2008), “Incorporating SubjectiveCharacteristics in Product Design and Evaluations,”Journal of Marketing Research, Vol.45, No.2, 182-194.* Donald R. Lehmann Award; Paul E. Green Award (finalist)8. Lan Luo, P. K. Kannan, and Brian T. Ratchford (2007), “New Product Development underChannel Acceptance,” Marketing Science, (Lead Article), Vol.26, No.2, 149-163.* John D.C. Little Award9. Babak Besharati, Lan Luo, Shapour Azarm, and P. K. Kannan (2006), “Multi-Objective SingleProduct Optimization: An Integrated Design and Marketing Approach,”ASME Journal ofMechanical Design, Special Issue: “Risk-Based and Robust Design”, Vol.128, No.4, 884-892.10. Lan Luo, P. K. Kannan, Babak Besharati, and Sh apour Azarm (2005), “Design of Robust NewProducts under Variability: Marketing Meets Design,”Journal of Product InnovationManagement, Special Issue: “Marketing Meets Design”, Vol.22, No.2, 177-192.Working Papers (Available Upon Request)11. Courtney Paulson, Lan Luo, and Gareth M. James (2016), “Optimal Large-Scale Internet MediaSelection,” under preparation for 2nd round review, Journal of Marketing Research.* ISMS Doctoral Dissertation Award; ASA Statistics in Marketing Travel AwardNatasha Foutz, Lan Luo, and Gerard J. Tellis (2016), “Virtual Stock Markets of New ProductSelected Work in Progress13. “Design of Multifunctional Products,” with P.K. Kannan, model development in progress.14. “Machine Learning of Successful New Product Launches,” with Dongling Huang, data collectionin progress.15. “Consumer Confidence, Monetary Expenditure, and Time Use over Economic Cycle,” withBotao Yang and Brian T. Ratchford, model development in progress.16. “Can Yelp Reviews Predict Restaurant Survival? Deep Learning of Online Word of Mouth,” withMengxia Zhang, model development in progress.Research InterestsSubstantive Areas: New Product Development, Marketing Implications of New Product Introductions Methods: Quantitative Modeling, Econometric Methods, Machine Learning, Marketing AnalyticsTeaching∙Marketing Analytics (Spring 2016), University of Southern California* A newly developed elective course for MBA and M.S. in Business Analytics students* Emphasis on hands-on approaches with real-world marketing analytics problems anddatasets∙Marketing Analysis and Strategy, University of Southern California, 2006 – present.* Highest Instructor Rating: 4.83/5.0 (Latest: 4.59/5.0)* Marshall Golden Apple Award for Teaching Excellence (2007): presented every year to the professors who have had the greatest impact on their students, as determined by themembers of the graduating class∙Marketing Research Methods, University of Maryland, 2004.* Instructor Rating: 4.4/5.0∙Quantitative Models in Marketing (Guest Lecturer), Ph.D. seminar, University of Southern California, 2009, 2011∙Engineering Decision Making (Guest Lecturer), Department of Mechanical Engineering, University of Maryland, 2005.Invited TalksChina Europe International Business School, May 2016 (scheduled)Georgetown University, October 2015Eighth Annual UT Dallas FORMS Conference, February 2014MIT, Sloan School of Management, May 2013University of Texas at Austin, McCombs School of Business, April 2013University of British Columbia, Sauder School of Business, February 2013Seventh Annual UT Dallas FORMS Conference, Discussant, February 2013Harvard Business School, Boston, MA, March 2012University of Maryland, College Park, MD, March 2012Ninth Annual Product and Service Innovation Conference, Utah, February 2012MSI 50th Anniversary Special Session, INFORMS Marketing Science Conference, June 2011 Santa Clara University, Santa Clara, CA, May 2011Cornell University, Johnson School of Management, Ithaca, NY, February 2011MSI Young Scholar Program, Park City, Utah, January 2011Washington University, Olin School of Business, St. Louis, Missouri, May 2010Seventh Annual Product and Service Innovation Conference, Park City, Utah, February 2010 UCLA Entertainment & Media Management Institute Workshop, November 2009Rensselaer Polytechnic Institute, Lally School of Management & Technology, NY, October 2009 First Annual Marketing Innovation Conference, Rensselaer Polytechnic Institute, May 2008 Fourth Annual Product and Service Innovation Conference, Solitude, Utah, February 2007 Indiana University, Kelley School of Business, October 2004University of Massachusetts at Amherst, Isenberg School of Management, October 2004MIT, Sloan School of Management, September 2004Northwestern University, Kellogg School of Management, September 2004University of Southern California, Marshall School of Business, September 2004University of Central Florida, College of Business Administration, September 2004University of Texas at Dallas, School of Management, September 2004Conference/Workshop Presentation and Participation2016∙INFORMS Marketing Science Conference, Shanghai, June 20162015∙INFORMS Marketing Science Conference, Baltimore, June 20152014∙Quantitative Marketing and Economics Conference, University of Southern California, October 2014∙INFORMS Marketing Science Conference, Atlanta, June 2014∙Eighth Annual UT Dallas FORMS Conference, February 2014∙Eleventh Annual Product and Service Innovation Conference, Utah, January 20142013∙INFORMS Marketing Science Conference, Istanbul, Turkey, July 2013∙Summer Institute of Competitive Strategy, UC Berkeley, June 2013∙Columbia University Marketing Department Brownbag, May 2013∙Seventh Annual UT Dallas FORMS Conference, University of Texas at Dallas, Discussant, February 20132012∙Quantitative Marketing and Economics Conference, Duke University, October 2012∙American Marketing Association ARTS Forum, Seattle, WA, June 2012∙INFORMS Marketing Science Conference Special Session, Boston, MA, June 2012∙Sixth Annual UT Dallas FORMS Conference, University of Texas at Dallas, February 2012∙Ninth Annual Product and Service Innovation Conference, Utah, February 20122011∙Quantitative Marketing and Economics Conference, University of Rochester, September 2011 ∙USC Marshall's Inaugural Faculty Research Fair, August, 2011∙Summer Institute of Competitive Strategy, UC Berkeley, July 2011∙INFORMS Marketing Science Conference, Houston, TX, June 2011∙UC-USC Marketing Colloquium, USC, April 2011∙Marketing Science Institute Young Scholar Program, Park City, Utah, January 20112010∙Quantitative Marketing and Economics Conference, UCLA, October 2010∙INFORMS Marketing Science Conference, University of Cologne, Germany, June 2010∙Seventh Annual Product and Service Innovation Conference, Park City, Utah, February 2010 2009∙UCLA Entertainment & Media Management Institute Workshop, November 2009∙PDMA Research Forum, October 2009∙INFORMS Annual Meeting, San Diego, October 2009∙Quantitative Marketing and Economics Conference, University of Chicago, October 2009∙Rensselaer Polytechnic Institute Research Seminar Series, October 2009∙Summer Institute of Competitive Strategy, UC Berkeley, July 2009∙INFORMS Marketing Science Conference, University of Michigan, June 2009∙Third Annual UT Dallas FORMS Conference, University of Texas at Dallas, February 20092008∙First Annual Marketing Innovation Conference, Rensselaer Polytechnic Institute, May 2008∙Second Annual UT Dallas FORMS Conference, University of Texas at Dallas, 2008∙Fifth Annual Product and Service Innovation Conference, Midway, Utah, February 20082007∙Quantitative Marketing and Economics Conference, University of Chicago, October 2007∙INFORMS Marketing Science Conference, Singapore Management University, June 2007∙UC-USC Marketing Colloquium, USC, April 2007∙Accelerating Market Acceptance in a Networked World, Marketing Science Institute, Los Angeles, March 2007∙Fourth Annual Product and Service Innovation Conference, Solitude, Utah, February 20072006∙INFORMS Annual Meeting, Pittsburgh, November 2006∙Summer Institute of Competitive Strategy, UC Berkeley, July 2006∙INFORMS Marketing Science Conference, Pittsburgh, June 2006∙UC-USC Marketing Colloquium, UC Riverside, April 20062005∙INFORMS Marketing Science Conference, Emory University, June 20052004∙ASME Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Salt Lake City, Utah, September 2004∙INFORMS Marketing Science Conference, Rotterdam, The Netherlands, June 2004∙Washington D.C. Marketing Colloquium, May 20042003∙INFORMS Marketing Science Conference, University of Maryland, June 2003∙NSF Design, Service and Manufacturing Grantees and Research Conference, University of Alabama, January 2003Professional Service∙Editorial Review Board:- Customer Needs and Solutions∙Co-judge, USC Stevens Student Innovator Showcase, sponsored by USC Stevens Center for Innovation, October 2015∙Co-Judge, Innovation Coast Conference and Competition Semi-Finals, sponsored by the Center for Global Innovation at USC Marshall, Irvine, CA, May 2015∙Co-Chair, New Product Design and Development Track, American Marketing Association Summer Educator Meeting, San Francisco, CA, August 2014∙Ad Hoc Reviewer:- Journal of Marketing Research- Marketing Science- Management Science- Operations Research- ASME Journal of Mechanical Design- Journal of Retailing- International Journal of Marketing Research- Information Systems Research- Journal of Service Research- Annals of Operation Research- Research Policy- International Journal of Production Economics- Journal of Intelligent Manufacturing∙Conference Program Reviewer:- Proceedings of ASME International Design Engineering Technical Conference, 2011, 2012 - American Marketing Association Summer Educator Meeting2006, 2008∙Other Reviewing:- Proposal Application to Research Grant Council of Hong Kong, 2013- PDMA Doctoral Dissertation Proposal Competition, 2008∙Faculty Affiliate:- Lloyd Greif Research Center, Marshall School of Business, USCMarshall School and Departmental ServiceMarshall School of Business Faculty Council Committee, 2014 – presentMarketing Department Annual Performance Review Committee, 2015, 2016 (chair)Marketing Seminar Series Coordinator, 2011 – 2014Marketing Department Undergraduate Curriculum Review Committee, 2013Marketing Department Ph.D. Mentoring Subcommittee, 2013Marketing Department Ph.D. Admission Committee, 2007, 2008, 2009, 2011, 2015, 2016Marketing Department Chair Selection Committee, 2010Marshall School of Business Undergraduate Strategy and Curriculum Committee, 2009Marketing Department Faculty Recruiting Committee, 2006, 2009Marketing Department Website Coordinator, 2005Mentoring Activities∙Panel Speaker for Junior Faculty and Ph.D. Students Mentoring- INFORMS Marketing Science Conference, Women in Marketing Science Lunch, Theme: “Work Life Balance,” June 2014- USC Marshall M-POWER (Marshall Panels on Women's Experiences in Research) Workshop, Theme: “Research, Teaching, and Service: Prioritizing Your Skillset to Becomea Successful Academic,” September 2014- USC Marshall Mentoring Committee Workshop, Theme: “Working Toward Mid-Tenure,”September 2011∙Ph.D. Advising:Current Students: (expected graduation)- Amy Pei (2019), member, qualifying exam committee- Heng Zhang (2019), USC Data Science and Operations, member, qualifying exam committeeFormer Students: (graduation date)- Courtney Paulson (2016), USC Data Science and Operations, member, dissertation committeePlacement: University of Maryland- Yanwei (Wayne) Zhang (2015), member, dissertation committeePlacement: CNA Insurance- Dinakar Jayarajan (2014), member, dissertation committeePlacement: Illinois Institute of Technology- Yi Zhu (2013), member, dissertation committeePlacement: University of Minnesota- Abhishek Borah (2013), member, dissertation committeePlacement: University of Washington- Sean Coary (2013), member, dissertation committeePlacement: St. Joseph’s University- Linli Xu (2012), member, dissertation committeePlacement: University of Minnesota- Seshadri Tirunillai (2011), member, dissertation committeePlacement: University of Houston- Ohjin Kwon (2010), member, dissertation committeePlacement: Concordia University- Shui Ki Wan (2010), USC Economics, member, dissertation committeePlacement: Hong Kong Baptist University- Deepa Chandrasekaran (2007), member, dissertation committeePlacement: Lehigh UniversityUSC Faculty Advisor:- Association of Innovative Marketing, May 2014 – present- Trojans Advertising Group, April 2014 – present- Chinese Student Association, April 2007 – present- Singapore Student Association, November 2006 – presentProfessional ExperienceResearch and Consulting / Black&Decker Co., under Co-sponsorship of National Science Foundation, 2002-2005.Project Coordination and Supervision / Jin Ling Petrochemical Industry Co., China, 1998. Professional MembershipsAmerican Marketing Association (AMA), 2005 - presentInstitute for Operations Research and Management Science (INFORMS), 2004 - presentProduct Development and Management Association (PDMA), 2004 – present。
如何写dissertation proposal
Some good and bad examples
(Proposals submitted in previous years)
• A cost-benefit analysis of the Medie Estate Development (Trump International Golf)
Structure of the proposals
• Title of dissertation • Motivation of dissertation topic • Originality of contribution • Dissertation structure • Description of the empirical chapter • Initial bibliography
Dissertation proposals
Alberto Paloni
It is highly recommended that students should refer to the Research Methods and Dissertation Training course documentation
o published results refer to the period before the financial crisis and this may have changed certain relationships [Explain why]
Dissertation structure
▫ The estate development is on a dune system declared a site of specific scientific interest. ▫ Defence based on the economic impact: employment in both short and long run. However, the unemployment rate in Aberdeenshire is very low. ▫ Criticism based on the environmental impact ▫ Motivation: no analysis so far has considered both impacts
论文的写作方法_怎么写Dissertation
论文的写作方法_怎么写Dissertation 出国后毕业论文的写作方法怎么写Dissertation?(英文Dissertation的写作方法)Dissertation, 写作一、Dissertation大体结构1.首先是Cover Page,即封面,包括论文的题目,作者姓名,所在学院,学校和上交时间等信息。
2.Declaration,也就是声明,“我声明,所有成果除了我已经注释的,其他均为我自己的研究成果,没有剽窃他人的”,之后是上交时间,指导导师,签名,学院等。
3.Acknowledgment,写要感谢的人,导师是必须要感谢的,当然还可以感谢其他人,这个随意。
4.Content,目录,这个要两端对齐,整体格式一致,用Word中的目录索引功能自动生成。
一般三级目录就可以了。
比如3,3.1,3.1.2。
5.List of Figures,也就是所有图片的一个目录,包括所有在论文中出现的图片标题,不要把图片也贴上,只要标题。
比如:1.1 XXXX标题名称2.1 XXXX标题名称6.List of Tables,和上面类似,就是表格的目录。
(注:以上内容的页码为罗马数字,从下面的Abstract开始,为阿拉伯数字并从1重新开始,这个在word中可以设置。
目录第一条从Introduction开始,而不是Abstract,切记。
)7.Abstract,也就是摘要。
一般50-60页的论文摘要1页以内即可,最好控制在半页到3/4页,不要多。
8.Introduction,介绍部分。
9.Background 或者Literature Review,题目只要涉及这些方面即可,一般是这两个。
内容也是。
(具体写作我后面会具体说)。
10.Proposed Method也就是主体部分,题目自定或者问问导师,但是内容是你的研究成果的描述。
11.Analytical and Test Results,分析和测试结果,题目自定,内容主要就是测试结果。
ap算法代码
ap算法代码AP算法(Affinity Propagation algorithm)是一种用于聚类分析的算法。
它是由Frey和Dueck于2007年提出的一种基于图理论的聚类算法。
AP算法通过将样本点视为网络中的节点,并计算节点之间的相似度来实现聚类分析。
本文将详细介绍AP算法的原理和实现过程,并通过一个示例来说明其应用。
一、算法原理AP算法的核心思想是基于信息传递的聚类分析。
在AP算法中,每个样本点既可以作为一个"exemplar"(代表点),也可以作为其他点的"support"(支持点)。
算法通过迭代计算每个样本点作为"exemplar"和"support"的可信度,并最终确定每个样本点的类别。
具体来说,AP算法的过程如下:1. 初始化相似度矩阵S,其中S(i,j)表示样本点i和j之间的相似度。
初始时,S(i,j)可以取任意值。
2. 初始化可信度矩阵R和吸引度矩阵A,其中R(i,j)表示样本点i 选择样本点j作为其"exemplar"的可信度,A(i,j)表示样本点i选择样本点j作为其"support"的吸引度。
初始时,R和A都设为0。
3. 迭代更新R和A,直到收敛。
更新规则如下:- 更新可信度矩阵R:对于每个样本点i,计算其选择各个样本点j作为其"exemplar"的可信度,即R(i,j) = S(i,j) - max{A(i,k) + S(i,k)},其中k≠j。
- 更新吸引度矩阵A:对于每个样本点i,计算其被各个样本点j选择为"support"的吸引度,即A(i,j) = min{0, R(j,j) + Σ[max{0, R(k,j)}]},其中k≠i且k≠j。
4. 根据R和A确定每个样本点的类别。
将R的每一行中,可信度最大的样本点作为该行对应样本点的"exemplar",将A的每一列中,吸引度最大的样本点作为该列对应样本点的"support"。
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You must always explain why such application is important
o e.g.: published results refer to country samples with developed and less-developed countries but the two groups may be different [Explain why] o published results refer to the period before the financial crisis and this may have changed certain relationships [Explain why]
• It should give some information about the content of the various chapters
o Introduction
The introduction explains the motivation of the dissertation (say what it is)
Dissertation structure
• It is not a simple list of chapters
e.g.: 1. Introduction; 2. Literature survey; 3. The economy of country X (if there is such a chapter); 4. Empirical analysis; 5. Conclusions
The empirical chapter
• What data do you intend to use?
o Be precise! e.g.: NOT a vague ‘World Bank data’ or ‘data on country X’; rather, specify the precise variables you are going to use (e.g., real GDP, PPP corrected, from World Development Indicators) Make a longer list of variables (you need alternatives!) o Make sure that you have access to these data
Structure of the proposals
• Title of dissertation • Motivation of dissertation topic • Originality of contribution • Dissertation structure • Description of the empirical chapter • Initial bibliography
Dissertation proposals
Alberto Paloni
It is highly recommended that students should refer to the Research Methods and Dissertation Training course documentation
Your supervisor
•
What if you have not done enough work to prepare a good proposal?
“In their meetings (with their supervisors), it is the students who are expected to take an active role. For example, while supervisors can give advice on the exact specification of the topic of the dissertation and the form of the empirical chapter, they will not resolve the problem for the students if their proposal is not well specified, i.e. if it is very broad and unfocused, there is not a clear research hypothesis, the applied chapter is not defined, etc. Students are expected to take the lead in the discussions with their supervisors, rather than passively waiting for the supervisor’s suggestions. For example, if the student's proposal provides no evidence that the student has been searching and reading some literature, the supervisor will not tell the student how to turn a vague proposal into a tight dissertation. The student must make the first step.” (From the Research Methods and Dissertation Training handbook)
Title of dissertation
• Focussed and narrow research question
o One question o Do not use broad titles o Make the reader understand immediately what the dissertation is about
The next slide explains how to access a bibliographic search engine
Bibliographic searches
• • • • •
•
Go to the library web site at Under ‘how to find information’ click on ‘Databases A to Z’. In the Search box type ‘Econlit’, then click ‘Search’ Click ‘Connect to EconLit (EBSCO host)’ Tick: ‘EconLit’, ‘Business Source Premiere’ (and, if you are looking for Sociology or Politics publications, also ‘SocINDEX with Full Text’); then click ‘Continue’ At this point you are in the database. You should choose to do an ‘Advanced Search’.
(continued)
• What do you want to use these data and techniques for?
o Is this analysis relevant for your topic? What conclusions can you expect to be able to draw from this analysis?
Motivation of the topic
• Why is the topic important?
o Normally because there is a debate in the literature with contrasting views o Never because you wanted to know more about the topic o Beware of scarce literature
• What technique(s) are you planning to use?
o Be precise! e.g.: NOT econometric/qualitative techniques or simply OLS; instead, explain the form of the regression or the averages you want to compare, the questions you want to ask and how you are going to analyse the responses o Be realistic! Are you competent enough?
Initial bibliography
• This should allow you and your supervisor to assess whether your bibliographic search has been comprehensive enough.
o It must include what you believe to be the most important references and, in particular, the ‘benchmark study(-ies)’ which you are basing your dissertation on. o Be honest! Write only what you are likely to read.
(continued)