1、軐( (愀漀讀缁缀螏頀h倀椀焂匃娑匀吀極酲晦彎搀漀漀焀瀀搀昀瀀椀挀最椀昀娀匀吀極酲晦彎搀漀漀焀瀀搀昀尀尀昀攀挀攀戀昀戀戀戀昀昀愀瀀砀娀瘀圀挀氀圀挀欀吀琀嘀唀匀眀攀眀昀戀嘀瘀焀漀稀搀倀眀甀堀氀戀一儀娀匀吀極鄀晦开搀漀漀焀昀搀挀攀昀愀昀戀昀搀攀愀攀挀攀挀圀繎歶極聹婢匀吀倀鄀瀀甀爀椀琀礀攀儀甀椀挀欀伀瘀攀爀瘀椀攀眀娀匀吀椀猀愀渀吀倀挀漀洀瀀攀琀椀琀椀瘀攀椀渀栀椀戀椀琀漀爀漀昀挀氀愀猀猀瀀栀漀猀瀀栀愀琀椀搀礀氀椀渀漀猀椀琀漀氀欀椀渀愀猀攀椀猀漀昀漀爀洀猀倀爀漀搀甀挀琀猀愀爀攀昀漀爀氀愀戀漀爀愀琀漀爀礀爀攀猀攀愀爀挀栀甀猀攀漀渀氀礀一漀琀昀漀爀栀甀洀愀渀甀猀攀圀攀搀漀渀漀琀猀攀氀氀琀漀瀀愀琀椀攀渀琀猀倀
2、爀漀搀甀挀琀渀愀洀攀娀匀吀圀琀漀爀洀甀氀愀一伀倀甀爀椀琀礀最琀匀琀漀爀愀最攀愀琀礀攀愀爀猀匀一漀洀攀爀最攀渀挀礀倀栀漀渀攀昀瀀渀匀洀椀氀攀猀挀漀搀攀伀一一一一一一一伀栀攀洀椀挀愀氀一愀洀攀椀昀氀甀漀爀漀洀攀琀栀礀氀戀攀渀稀椀洀椀搀愀稀漀氀礀氀搀椀洀漀爀瀀栀漀氀椀渀漀琀爀椀愀稀椀渀攀匀漀氀甀戀椀氀椀琀礀渀瘀椀琀爀漀匀伀洀最洀洀圀愀琀攀爀氀琀洀最洀氀琀洀琀栀愀渀漀氀氀琀洀最洀氀琀洀渀瘀椀瘀漀栀礀搀爀漀砀礀攀琀栀礀氀挀攀氀氀甀氀漀猀攀洀最洀氀琀洀最洀氀洀攀愀渀猀猀氀椀最栀琀氀礀猀漀氀甀戀氀攀漀爀椀渀猀漀氀甀戀氀攀倀氀攀愀猀攀渀漀琀攀琀栀愀琀搀漀漀焀琀攀猀琀猀琀栀攀猀漀氀甀戀椀氀椀琀礀漀昀愀氀氀挀漀洀
3、瀀漀甀渀搀猀椀渀栀漀甀猀攀愀渀搀琀栀攀愀挀琀甀愀氀猀漀氀甀戀椀氀椀琀礀洀愀礀搀椀昀昀攀爀猀氀椀最栀琀氀礀昀爀漀洀瀀甀戀氀椀猀栀攀搀瘀愀氀甀攀猀吀栀椀猀椀猀渀漀爀洀愀氀愀渀搀椀猀搀甀攀琀漀猀氀椀最栀琀戀愀琀挀栀琀漀戀愀琀挀栀瘀愀爀椀愀琀椀漀渀猀匀漀氀甀戀椀氀椀琀礀吀栀攀猀栀椀瀀瀀椀渀最愀渀搀栀愀渀搀氀椀渀最昀攀攀椀猀唀匀昀漀爀猀栀椀瀀洀攀渀琀猀眀椀琀栀椀渀琀栀攀唀匀愀渀搀唀匀唀刀昀漀爀猀栀椀瀀洀攀渀琀猀漀甀琀猀椀搀攀唀匀氀氀漀甀爀瀀爀漀搀甀挀琀猀眀椀氀氀戀攀猀栀椀瀀瀀攀搀漀甀琀昀爀漀洀爀瘀椀渀攀唀匀伀爀搀攀爀猀眀椀氀氀戀攀猀栀椀瀀瀀攀搀搀椀爀攀挀琀氀礀琀漀礀漀甀瘀椀愀唀倀匀攀搀砀漀爀匀栀椀瀀洀攀
4、渀琀琀椀洀攀猀愀爀攀愀瀀瀀爀漀砀椀洀愀琀攀氀礀戀甀猀椀渀攀猀猀搀愀礀猀倀愀挀欀愀最攀猀愀渀搀瀀爀漀搀甀挀琀猀猀栀漀甀氀搀戀攀椀渀猀瀀攀挀琀攀搀椀洀洀攀搀椀愀琀攀氀礀甀瀀漀渀爀攀挀攀椀瀀琀一漀琀椀昀椀挀愀琀椀漀渀漀昀搀愀洀愀最攀猀栀漀爀琀愀最攀猀漀爀搀攀昀攀挀琀猀猀栀漀甀氀搀戀攀猀攀渀琀琀漀甀猀椀洀洀攀搀椀愀琀攀氀礀戀礀攀洀愀椀氀漀爀昀愀砀爀攀攀猀栀椀瀀瀀椀渀最瀀漀氀椀挀礀一漀眀搀漀漀焀漀昀昀攀爀昀爀攀攀猀栀椀瀀瀀椀渀最眀漀爀氀搀眀椀搀攀渀礀漀爀搀攀爀漀瘀攀爀唀匀唀刀倀椀猀焀甀愀氀椀昀椀攀搀昀漀爀昀爀攀攀猀栀椀瀀瀀椀渀最礀驲葛塶豐葔潏晦灙搀漀漀焀偣蒃慶塎驺塏瑎蒋塐灙塐釿幒蹜晓鎏塐桎敥譑杣灔湥档蒈
5、厐塟堰愀谀卦栀椀瀀瀀椀渀最愀渀搀攀氀椀瘀攀爀礀圀繎歶極聹遒娀-裴(H邀敄)詐-輀8棙$i缀$Tivozanib （AV-951）_VEGFR抑制剂_CAS 475108-18-0说明书_Adooq.pdf0.jpgTivozanib(AV951)_VEGFR抑制剂_CAS475108180说明书_Adooq.pdf2018-122098b4df31-0d2f-4f65-a67e-9a10554f7f1eKfQ0heQufKb1DaMK3x1mFfXVDCwRb9HS1lqprhxxG2FZ4+4x7lYIfg=tivozanib,AV-95135f009fdf23821777c516fbc34
6、d0c0a71卓越小分子抑制剂&分子库供应商Tivozanib (AV-951) VEGFR抑制剂 purity 98%Product name Tivozanib (AV-951)M. Wt 454.9Formula C22H19ClN4O5Purity >98+%Storage at -20 C 2 yearsCAS No 475108-18-0Emergency Phone#： 400-025-6535Synonyms AV-951, AV951smiles code 1-2-Chloro-4-(6,7-dimethoxyquinolin-4-yl)oxyphenyl-3-
7、(5-methylisoxazol-3-yl)ureaChemical Name CC1=CC(=NO1)NC(=O)NC2=C(C=C(C=C2)OC3=C4C=C(C(=CC4=NC=C3)OC)OC)Cl化 学 数 据21. The shipping and handling fee is 30USD for shipments within the USAand 40USD (38EUR) for shipments outside USA.2. All our products will be shipped out from Irvine, CA USA.3. Orders wil
8、l be shipped directly to you via UPS, FedEx or DHL. Shipmenttimes are approximately 2-3 business days.Solubility (25C) * In vitro DMSO 20 mg/mL (43.96 mM)Water 98+%,品质稳定,仅供研究所、科研单位、实验室、学校等人员研究使用。存 储 条 件 和 包 装 说 明南京百鑫德诺生物科技0001200004市场分析RGB20181220154812210T的时候只是沉醉在自我的世界中，侃侃而谈；而有的人却一边谈自己，一边能用眼神与考官交流。
9、前者表明他自我意识强，是个自我中心的人，或者也可能他存在一些社交上的心理困扰，不敢与人直面相对；而后者则在这一情境中表现出了明显的交流意图，他讲话的时候，并-专业最好文档，专业为你服务，急你所急，供你所需 -文档下载最佳的地方-专业最好文档，专业为你服务，急你所急，供你所需 -文档下载最佳的地方不是在自言自语，他会注意听众的反应。在实际的面试中，我所接触到的一位面试者曾给我留下了深刻的印象，他在自我介绍的时候，面带笑容，眼光在几个考官的身上缓缓走过，每个考官都觉得他是在注视着自己，向自己做着介绍，无疑，这是一位人际沟通的擅长者。2. 笑容笑容笑容笑容 笑容也很重要。很清楚，面试的时刻往往是一个
10、人窘迫的时候，虽然面试相关的书上会要求考官营造一个和谐的气氛，首先自己面带笑容应对面试者。但在真正的面试情境中，很多面试考官几乎连续几天都在搞面试工作，已经成了“面霸” 迼(3(砀愀欀鰀砀讀缁缀螏頀h倀椀笂甃甃甃攀洀挀椀琀愀戀椀渀攀氀攀洀稀愀爀开匀晦彎搀漀漀焀瀀搀昀瀀椀挀最椀昀攀洀挀椀琀愀戀椀渀攀氀攀洀稀愀爀开匀晦彎搀漀漀焀瀀搀昀尀尀挀搀挀挀愀昀戀一唀稀愀夀儀椀漀渀瘀娀栀攀瘀椀爀攀椀椀樀焀搀甀栀栀礀洀栀甀稀愀匀昀夀昀最最攀洀挀椀琀愀戀椀渀攀栀挀氀最攀洀稀愀爀戀攀搀愀挀搀攀戀愀愀挀愀愀挀匀言倀鄀愀洀瀀倀錀鬀鐀愀琀愀氀漀最一甀洀倀爀漀搀甀挀琀渀愀洀攀攀洀挀椀琀愀戀椀渀攀氀攀洀稀愀爀圀琀漀爀洀甀氀愀
11、一伀倀甀爀椀琀礀最琀匀琀漀爀愀最攀愀琀礀攀愀爀猀匀一漀洀攀爀最攀渀挀礀倀栀漀渀攀猀洀椀氀攀猀挀漀搀攀一伀一一嬀崀嬀崀嬀崀伀伀伀氀栀攀洀椀挀愀氀一愀洀攀洀椀渀漀嬀搀椀昀氀甀漀爀漀栀礀搀爀漀砀礀栀礀搀爀漀砀礀洀攀琀栀礀氀琀攀琀爀愀栀礀搀爀漀昀甀爀愀渀礀氀崀瀀礀爀椀洀椀搀椀渀漀渀攀栀礀搀爀漀挀栀氀漀爀椀搀攀昀瀀渀吀栀攀猀栀椀瀀瀀椀渀最愀渀搀栀愀渀搀氀椀渀最昀攀攀椀猀唀匀昀漀爀猀栀椀瀀洀攀渀琀猀眀椀琀栀椀渀琀栀攀唀匀愀渀搀唀匀唀刀昀漀爀猀栀椀瀀洀攀渀琀猀漀甀琀猀椀搀攀唀匀氀氀漀甀爀瀀爀漀搀甀挀琀猀眀椀氀氀戀攀猀栀椀瀀瀀攀搀漀甀琀昀爀漀洀爀瘀椀渀攀唀匀伀爀搀攀爀猀眀椀氀氀戀攀猀栀椀瀀瀀攀搀搀椀爀攀挀琀氀
12、礀琀漀礀漀甀瘀椀愀唀倀匀攀搀砀漀爀匀栀椀瀀洀攀渀琀琀椀洀攀猀愀爀攀愀瀀瀀爀漀砀椀洀愀琀攀氀礀戀甀猀椀渀攀猀猀搀愀礀猀匀漀氀甀戀椀氀椀琀礀渀瘀椀琀爀漀匀伀驺魎問鸰豛昰塎癸罺頀壿愀谀垈繎歶極聹遒軐(槴)迼(軐(胔-輀+棙$i缀$菝2018-2024年中国煤化工PPP模式行业全景调研及市场供需预测报告(目录).docpic1.gif20182024年中国煤化工PPP模式行业全景调研及市场供需预测报告(目录).doc2018-12176e8f1b63-ef5b-4f0e-8a20-61e6599b6d302rGKEX4tF6+Zzkxy1Qn7Z6wo3QbdJWwv+iXi+lQ7pJZoFWgo4
13、1P5A=2018,2024,年中,煤化工,PPP,模式,行业,全景,调研,市场,供需,预测,报告,目录634d462e6c9c91f3dbbf2ccb9a951a75酲志在邹0001200001可研报告20181217203537461504?0迼(I(愀鸀翸讀缁螏頀h椀漂甃甃甃锃锊锊愀爀愀瘀椀爀漀挀唀开刀面归开搀漀漀焀瀀搀昀瀀椀挀最椀昀愀爀愀瘀椀爀漀挀唀开刀面归开搀漀漀焀瀀搀昀尀尀昀昀昀戀攀愀挀攀攀愀搀戀栀氀一儀甀伀漀氀欀砀愀樀搀砀刀猀眀礀欀漀唀砀搀欀匀爀渀爀洀愀爀愀瘀椀爀漀挀唀唀搀戀戀戀昀攀昀愀搀昀挀愀攀昀匀言倀鄀愀洀瀀倀錀鬀鐀偕爀漀搀甀挀琀渀愀洀攀愀爀愀瘀椀爀漀挀唀圀琀漀爀洀甀氀愀一伀
14、倀甀爀椀琀礀最琀匀琀漀爀愀最攀愀琀礀攀愀爀猀匀一漀洀攀爀最攀渀挀礀倀栀漀渀攀匀礀渀漀渀礀洀猀唀攀氀猀攀渀琀爀椀猀洀椀氀攀猀挀漀搀攀一一一嬀崀嬀崀一嬀崀一伀栀攀洀椀挀愀氀一愀洀攀搀椀昀氀甀漀爀漀一笀匀嬀椀猀漀瀀爀漀瀀礀氀洀攀琀栀礀氀琀爀椀愀稀漀氀礀氀愀稀愀戀椀挀礀挀氀漀嬀崀漀挀琀礀氀崀瀀栀攀渀礀氀瀀爀漀瀀礀氀紀挀礀挀氀漀栀攀砀愀渀攀挀愀爀戀漀砀愀洀椀搀攀昀瀀渀吀栀攀猀栀椀瀀瀀椀渀最愀渀搀栀愀渀搀氀椀渀最昀攀攀椀猀唀匀昀漀爀猀栀椀瀀洀攀渀琀猀眀椀琀栀椀渀琀栀攀唀匀愀渀搀唀匀唀刀昀漀爀猀栀椀瀀洀攀渀琀猀漀甀琀猀椀搀攀唀匀氀氀漀甀爀瀀爀漀搀甀挀琀猀眀椀氀氀戀攀猀栀椀瀀瀀攀搀漀甀琀昀爀漀洀爀瘀椀渀攀唀匀
15、伀爀搀攀爀猀眀椀氀氀戀攀猀栀椀瀀瀀攀搀搀椀爀攀挀琀氀礀琀漀礀漀甀瘀椀愀唀倀匀攀搀砀漀爀匀栀椀瀀洀攀渀琀琀椀洀攀猀愀爀攀愀瀀瀀爀漀砀椀洀愀琀攀氀礀戀甀猀椀渀攀猀猀搀愀礀猀匀漀氀甀戀椀氀椀琀礀渀瘀椀琀爀漀匀伀洀最洀洀圀愀琀攀爀驺魎問鸰豛昰塎癸罺頀壿愀谀垈繎歶極聹遒軐(槴)迼(軐(胔-輀+棙$i缀$菝2018-2024年中国煤化工PPP模式行业全景调研及市场供需预测报告(目录).docpic1.gif20182024年中国煤化工PPP模式行业全景调研及市场供需预测报告(目录).doc2018-12176e8f1b63-ef5b-4f0e-8a20-61e6599b6d302rGKEX4tF6+Zzk
16、xy1Qn7Z6wo3QbdJWwv+iXi+lQ7pJZoFWgo41P5A=2018,2024,年中,煤化工,PPP,模式,行业,全景,调研,市场,供需,预测,报告,目录634d462e6c9c91f3dbbf2ccb9a951a75酲志在邹0001200001可研报告20181217203537461504?题(本大题共 3 小题，每小题 14 分，共 42 分) 30.阅读材料，并回答问题。李老师认为，要让孩子树立自信心，就必须让孩子发现自己的优点，在一次课上，李老师组织学生讨论：“你有哪些优点?”同学们讨论得非常激烈，有的说自己乐于助人， 有的说自己孝顺父母，有的说自己尊敬老师大家发
17、现原来自己和小伙伴都有很多优点呢。这时，一向活泼好动的小明把手举得很高，李老师说：“小明，你说说自己有哪些优点?”小明说： “你为什么总是叫我们说优点呢?”我爸爸说，每个人有缺点，老师也有缺点，你也有，我想说缺点。”教室里一下安静了，k loops with positional features. Rule #37: Measure Training/Serving Skew. ML Phase III: Slowed Growth, Optimization Refinement, and Complex Models Rule #38: Dont waste time on new fe
18、atures if unaligned objectives have become the issue. Rule #39: Launch decisions will depend upon more than one metric. Rule #40: Keep ensembles simple. Rule #41: When performance plateaus, look for qualitatively new sources of information to add rather than refining existing signals. Rule #42: Dont
19、 expect diversity, personalization, or relevance to be as correlated with popularity as you think they are. Rule #43: Your friends tend to be the same across different products. Your interests tend not to be. Related Work Acknowledgements Appendix YouTube Overview Google Play Overview Google Plus Ov
20、erview Terminology The following terms will come up repeatedly in our discussion of effective machine learning: Instance : The thing about which you want to make a prediction. For example, the instance might be a web page that you want to classify as either “about cats“ or “not about cats“. Label :
21、An answer for a prediction task either the answer produced by a machine learning system, or the right answer supplied in training data. For example, the label for a web page might be “about cats“. Feature : A property of an instance used in a prediction task. For example, a web page might have a fea
22、ture “contains the word cat“. Feature Column : A set of related features, such as the set of all possible countries in which 1users might live. An example may have one or more features present in a feature column. A feature column is referred to as a “namespace” in the VW system (at Yahoo/Microsoft)
23、, or a field . Example : An instance (with its features) and a label. Model : A statistical representation of a prediction task. You train a model on examples then use the model to make predictions. Metric : A number that you care about. May or may not be directly optimized. Objective : A metric tha
24、t your algorithm is trying to optimize. Pipeline : The infrastructure surrounding a machine learning algorithm. Includes gathering the data from the front end, putting it into training data files, training one or more models, and exporting the models to production. Overview To make great products: d
25、o machine learning like the great engineer you are, not like the great machine learning expert you arent. 1 Googlespecific terminology. Most of the problems you will face are, in fact, engineering problems. Even with all the resources of a great machine learning expert, most of the gains come from g
26、reat features, not great machine learning algorithms. So, the basic approach is: 1. make sure your pipeline is solid end to end 2. start with a reasonable objective 3. add commonsense features in a simple way 4. make sure that your pipeline stays solid. This approach will make lots of money and/or m
27、ake lots of people happy for a long period of time. Diverge from this approach only when there are no more simple tricks to get you any farther. Adding complexity slows future releases. Once youve exhausted the simple tricks, cuttingedge machine learning might indeed be in your future. See the secti
28、on on Phase III machine learning projects. This document is arranged in four parts: 1. The first part should help you understand whether the time is right for building a machine learning system. 2. The second part is about deploying your first pipeline. 3. The third part is about launching and itera
29、ting while adding new features to your pipeline, how to evaluate models and trainingserving skew. 4. The final part is about what to do when you reach a plateau. 5. Afterwards, there is a list of related work and an appendix with some background on the systems commonly used as examples in this docum
30、ent. Before Machine Learning Rule #1: Dont be afraid to launch a product without machine learning. Machine learning is cool, but it requires data. Theoretically, you can take data from a different problem and then tweak the model for a new product, but this will likely underperform basic heuristics.
31、 If you think that machine learning will give you a 100% boost, then a heuristic will get you 50% of the way there. For instance, if you are ranking apps in an app marketplace, you could use the install rate or number of installs. If you are detecting spam, filter out publishers that have sent spam
32、before. Dont be afraid to use human editing either. If you need to rank contacts, rank the most recently used highest (or even rank alphabetically). If machine learning is not absolutely required for your product, dont use it until you have data. Rule #2: First, design and implement metrics. Before
33、formalizing what your machine learning system will do, track as much as possible in your current system. Do this for the following reasons: 1. It is easier to gain permission from the systems users earlier on. 2. If you think that something might be a concern in the future, it is better to get histo
34、rical data now. 3. If you design your system with metric instrumentation in mind, things will go better for you in the future. Specifically, you dont want to find yourself grepping for strings in logs to instrument your metrics! 4. You will notice what things change and what stays the same. For inst
35、ance, suppose you want to directly optimize oneday active users. However, during your early manipulations of the system, you may notice that dramatic alterations of the user experience dont noticeably change this metric. Google Plus team measures expands per read, reshares per read, plusones per rea
36、d, comments/read, comments per user, reshares per user, etc. which they use in computing the goodness of a post at serving time. Also, note that an experiment framework, where you can group users into buckets and aggregate statistics by experiment, is important . See Rule #12 . By being more liberal about gathering metrics, you can gain a broader picture of your system. Notice a problem? Add a met
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