Open Graph的問題，透過圖書和論文來找解法和答案更準確安心。 我們挖掘到下列精選懶人包

FinTech金融科技名詞速查字典：全面即懂人工智慧、數位貨幣、區塊鏈、支付科技及網路安全

為了解決Open Graph的問題，作者金融科技理論與應用研究小組 這樣論述：

★涵蓋 11 項領域，121個主要用語詳解 ★數百條相關詞彙說明 + 中英名詞對照 ★跟上國際趨勢，一頁讀懂一則科技知識 數位貨幣有哪些？比特幣、挖礦，背後運用的正是金融科技關鍵技術之一的區塊鏈。 人工智慧包含有那些科技金融知識？包含時下耳熟能詳的大數據、區塊鏈、雲端運算、人工智慧等。 想像未來的金融運作模式嗎？付款有哪些支付方式？從傳統到數位，消費者隱私保護、網路安全、平台與數據壟斷到運算法，如網路銀行、開放銀行、加密貨幣、個人資料保護規則、防制洗錢以及監理沙盒等主題，讓你掌握最新金融科技趨勢。 最全面基礎的金融科技知識：對金融科技某個概念或問題產生興趣或疑問時，本書提供完整概念並建構

好思路。 提供系統化查詢：破除金融科技的盲點與迷思，可依需查詢時下熱門詞彙、應用領域、技術支援等。

Open Graph進入發燒排行的影片

布里斯洛中間體自由基反應機制之理論研究

為了解決Open Graph的問題，作者謝明修 這樣論述：

含氮雜環卡賓（N-heterocyclic carbene）催化之化學反應中，布里斯洛中間體（Breslow intermediate）扮演重要的催化角色。布里斯洛中間體能以親核基（nucleophile）或自由基（radical）之形式參與反應。本論文探討布里斯洛中間體之自由基特性及形成機制（mechanism），其自由基可從氫自由基轉移或直接氧化形成。安息香縮合反應（benzoin condensation）中，布里斯洛中間體將氫原子轉移至苯甲醛（benzaldehyde）以形成自由基，此自由基可結合形成安息香產物，或排除反應之副產物，使其重新進入催化反應。唯此路徑之反應能障高於傳統非自

由基路徑。此研究亦探討四種布里斯洛中間體之不同電子組態的位能面。其中烯醇鹽（enolate）形式能產生偶極束縛態（dipole-bound state），此為產生自由基之新路徑；拉電子基（electron-withdrawing group）以及立體障礙基（bulky groups）可穩定基態。另外，我們亦研究布里斯洛中間體之碎片化（fragmentation）與重組（rearrangement）。布里斯洛中間體之催化反應可能因其碳氮鍵斷裂而中止，形成碎片。我們證實其反應中可以形成自由基，亦可形成離子。反應趨向之路徑與布里斯洛中間體之羥基的質子化型態有關。碎片化反應亦可視為轉酮醇酶（tran

sketolase）中之噻胺（thiamin）催化反應中之副反應；此研究證實轉酮醇酶透過限制布里斯洛中間體之結構與質子化型態，使其碳氮鍵斷裂需更高之反應能量，進而抑制此副反應。

Kannur: Inside India’s Bloodiest Revenge Politics

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為了解決Open Graph的問題，作者Ullekh 這樣論述：

Kannur, a sleepy coastal district in the scenic south Indian state of Kerala, has metamorphosed into a hotbed of political bloodshed in the past few decades. Even as India heaves into the age of technology and economic growth, the town has been making it to the national news for horrific crimes and

brutal murders with sickening regularity. What makes this region so susceptible to vendetta politics and such deadly violence? How is it an anomaly in Kerala, the state with the highest social development parameters in India? Born in Kannur and brought up amidst some of the tallest political leader

s of the state, author Ullekh N.P. delves into his personal experiences while drawing a modern-day graph that charts out the reasons, motivations and the local lore behind the turmoil. He analyses the numbers that lay bare the truth behind the hype, studies the area’s political and cultural heritage

, and speaks to the main protagonists and victims. With his journalistic skills and years of on-the-field reporting, he paints a gripping narrative of the ongoing bloodbath and the perceptions around it. Ullekh’s investigations and interviews reveal a bigger game at work involving players who will

stop at nothing to win. Ullekh N.P. is a journalist and political commentator based in New Delhi. He has worked for almost two decades with India’s biggest news publications such as the Economic Times and India Today, and has travelled extensively across India and overseas reporting on domestic an

d international politics. He is the author of War Room: The People, Tactics and Technology behind Narendra Modi’s 2014 Win and The Untold Vajpayee: Politician and Paradox. He is the executive editor of Open magazine.

基於使用者表示法轉換之跨領域偏好排序於推薦系統

為了解決Open Graph的問題，作者陳先灝 這樣論述：

隨著電子商務、影像串流服務等線上服務平台的發展，各大服務供應商對於「精準掌握用戶喜好」等相關技術的需求也逐季提升。其中，推薦系統作為這類方法的核心技術，如何在多變的現實問題中，提出符合特定需求的解決方式，也成為近年來相關研究的主要方向。在本研究中，我們特別關心的是推薦系統中的冷啟動 (Cold Start) 問題。 冷啟動問題發生的主要原因，是因為特定情況造成的資料稀缺，比如推薦系統中的新用戶/物品等等。由於其困難性和實際應用中的無可避免，一直是推薦系統研究中，的一個具有挑戰性的問題。其中，緩解此問題的一種有效方法，是利用相關領域的知識來彌補目標領域的數據缺失問題，即所謂跨領域推薦 (Cro

ss-Domain Recommendation)。跨領域推薦的主要目的在於，在多個不同的領域中實行推薦演算法，從中描繪出用戶的個人偏好 (Personal Preference)，再利用這些資訊來補充目標領域缺少的數據，從而在某種程度上解決冷啟動問題。在本文中，我們提出了一個基於用戶轉換的的跨領域偏好排序方法（CPR），它讓用戶從源域 (Source Domain) 和目標域 (Target Domain)的物品中同時擷取資訊，並據此進行表示法學習，將其轉化為自身偏好的表示向量。通過這樣的轉換形式，CPR 將除了能有效地利用源域的資訊之外，也能直接地以此更新目標域中用戶和物品的相關表示，從而

有效地改善目標域的推薦成果。在數據實驗中，為了能有效證明 CPR 方法的能力，我們將 CPR 方法實驗在六個不同的工業級資料上，並在差異化的條件設定 (目標域全體、冷啟動用戶、共同用戶) 中進行測試，也以先進的跨領域和單領域推薦演算法做為比較基準，進行比較。最後發現，CPR 不僅成功提高目標域整體的推薦效能，針對特定的冷啟動用戶也達到相當好的成果。