Discovery, Inc的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列股價、配息、目標價等股票新聞資訊

Pharma and Profits: Balancing Innovation, Medicine, and Drug

為了解決Discovery, Inc的問題，作者Lamattina, John L. 這樣論述：

High-level commentary on various facets of the pharmaceutical industry from a key leader in the fieldThis book clearly explains the value that the pharmaceutical industry offers to society which is often underreported against the more negative topic of high drug prices. It also offers an overview fo

r drug discovery and development professionals, highlighting the challenges that such drug hunters should be aware of when developing new drugs. Case studies to illustrate topics like hepatitis C, mRNA vaccines, insulin, and price controls are included to aid in seamless reader comprehension. Writte

n by John LaMattina, former president of Pfizer Global Research and Development and well-known speaker and writer for the pharma industry, sample topics covered and questions explored within the work include: Fiscal consequences of curing hepatitis CmRNA vaccines and the race for a cureWhy the gover

nment does not deserve a piece of Biopharma’s profitsPaying for drugs whose ultimate value is unknownThe impact of reduced revenues on R&DThis book is a must-read for biopharmaceutical professionals and executives who wish to gain high-level insight into key challenges that must be first understood,

then overcome, within the pharmaceutical industry. John LaMattina, PhD is the former Senior Vice President, Pfizer Inc and President, Pfizer Global Research and Development. In this role, Dr. LaMattina oversaw the drug discovery and development efforts of over 12,000 colleagues in the United Stat

es, Europe and Asia. Currently a Senior Partner with PureTech Health, he is the author of Drug Truths: Dispelling the Myths About Pharma R&D (2008)and Devalued and Distrusted - Can the pharmaceutical industry restore its broken image? (2013), both published by Wiley.

Discovery, Inc進入發燒排行的影片

基於翻譯序列推薦模型於跨領域推薦系統之強化方法

為了解決Discovery, Inc的問題，作者王均捷 這樣論述：

若我們有足夠多的歷史資料，就可以用很多不同的方法去建立一個聰明的推薦系統。但在某些情況下，比如一個新的社交媒體平台或電商平台上線時，我們沒有足夠的使用者物品互動資料來建構出好的推薦系統。其中一個強化跨領域推薦（cross-domain recommendation）的解決方案，是藉由將「來源領域（資訊含量較多之領域）」的資料加入「目標領域（資訊量相對較少的領域）」來提升資訊量，然後對「目標領域」進行推薦。本論文採用圖形學習表示演算法，結合改良並善用翻譯序列推薦模型（Translation-based Recommendation，TransRec）的推薦優勢，特化模型訓練時採樣方法、改變翻譯

序列合併方法，並引入貝氏個人化推薦（Bayesian Personalized Ranking，BPR）中負採樣（negative sampling）的概念，訓練得到推薦系統任務導向之表示向量，藉此改善推薦結果。本研究旨在通過改良後的翻譯序列推薦模型「TransRecCross」來強化跨領域推薦效果。驗證本論文的新方法時，使用了 Amazon Review 系列資料集中的其中四個，並在論文最後比較了加入不同比例的來源領域資料後的推薦結果，以驗證本論文提出之方法的可靠程度。

Patent Analysis and Mining for Business Intelligence

為了解決Discovery, Inc的問題，作者Jin, Bo,Xiong, Hui 這樣論述：

This book provides a comprehensive compendium of recent research on business intelligence-oriented patent data analysis and mining. Through the book, the readers will gain an essential understanding of the following topics: (1) text mining modeling for patent documents, including statistics modeling

and key phrase extraction mining; (2) the patent retrieval method, including chuck based retrieval and retrieval fusion method; and (3) integrated business solutions for stock dynamics, technology prospecting, and minimizing legal exposure. This book provides an informative and insightful reference

guide for researchers who are newcomers to patent data mining and business intelligence, as well as for professionals and practitioners from industry. Dr. Bo Jin is an Associate Professor in Dalian University of Technology. He received his Ph.D. in Computer Science in 2009. His general area of re

search is data mining and knowledge discovery. He has published prolifically in refereed journals and conference proceedings (60+ papers), e.g., SIGKDD, ICDM, and PAKDD. He has served regularly in the program committees of a number of conferences and is a reviewer for the leading academic journals i

n his fields, e.g., SIGKDD, ICDM, DASFAA, SDM, TKDE, and SpringPlus. He is a senior member of ACM, IEEE, and CCF.Dr. Hui Xiong received his Ph.D. in Computer Science from the University of Minnesota - Twin Cities, USA, in 2005, the B.E. degree in Automation from the University of Science and Technol

ogy of China (USTC), Hefei, China, and the M.S. degree in Computer Science from the National University of Singapore (NUS), Singapore. He is currently a Professor and Vice Chair in the Management Science and Information Systems Department, and the Director of Rutgers Center for Information Assurance

, at Rutgers, the State University of New Jersey, where he received a two-year early promotion/tenure (2009), the Rutgers University Board of Trustees Research Fellowship for Scholarly Excellence (2009), the ICDM-2011 Best Research Paper Award (2011), an IBM ESA Innovation Award (2008), the Junior F

aculty Teaching Excellence Award (2007), the Junior Faculty Research Award (2008), and Dean’s Award for Meritorious Research (2010, 2011, 2013) at Rutgers Business School.Dr. Xiong’s general area of research is data and knowledge engineering, with a focus on developing effective and efficient data a

nalysis techniques for emerging data intensive applications. His research has been supported in part by the National Science Foundation (NSF), IBM Research, SAP Corporation, Panasonic USA Inc., Awarepoint Corp., Citrix Systems Inc., and Rutgers University. He has published prolifically in refereed j

ournals and conference proceedings, such as IEEE Transactions on Knowledge and Data Engineering, the VLDB Journal, INFORMS Journal on Computing, Machine Learning, the Data Mining and Knowledge Discovery Journal, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), SIAM I

nternational Conference on Data Mining (SDM), IEEE International Conference on Data Mining (ICDM), and ACM International Symposium on Advances in Geographic Information Systems (ACM GIS). He is a co-Editor-in-Chief of Encyclopedia of GIS (Springer, 2008) and an Associate Editor of IEEE Transactions

on Data and Knowledge Engineering (TKDE), IEEE Transactions on Big Data (TBD), ACM Transactions on Knowledge Discovery from Data (TKDD) and ACM Transactions on Management Information Systems (TMIS). He has served regularly on the organization and program committees of numerous conferences, including

as a Program Co-Chair of the Industrial and Government Track for the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), a Program Co-Chair for the IEEE 2013 International Conference on Data Mining (ICDM), and a General Co-Chair for the IEEE 2015 International Con

ference on Data Mining (ICDM). He is an ACM Distinguished Scientist and a senior member of the IEEE.

旅館的階段性競爭與轉型策略：嘉義縣佳仕堡商務飯店的案例

為了解決Discovery, Inc的問題，作者陳家宥 這樣論述：

本論文旨在以位於嘉義縣的佳仕堡飯店為個案，研究一家旅館在其歷經開發、發展、衰頹等各種不同生命週期的階段，在其面對來自內、外在各種壓力因子之下，如何重新定位，擬定何種轉型策略。論文的進行主要以檔案分析法、深度訪談法、參與觀察法等蒐集資料，並進行描述與分析。研究結果為：佳仕堡飯店的轉型，是利用各 階段的市場趨勢重新定位市場，針對目標客群改變及調整所需的軟硬體設備，更以優勢的空間地點來提供其它市區飯店不同的差異化服務，因而使佳仕堡商務飯店的轉型獲得成功，本研究結果，有助於汽車旅館業者或一般旅館在轉型策略方面中作為參考