Shelf的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列問答集和資訊懶人包

Once More Around the Park: A Baseball Reader

為了解決Shelf的問題，作者Angell, Roger 這樣論述：

The most celebrated baseball writer of our time has selected his favorite pieces from the last forty years to create Once More Around the Park, a definitive volume of his most memorable work. Mr. Angell includes writing never previously collected as well as selections from The Summer Game, Five S

easons, Late Innings, and Season Ticket. He brings back the extraordinary games, innings and performances that he has witnessed and written about so astutely and gracefully--＂The Interior Stadium,＂ on the complex attractions of baseball; ＂In the Country,＂ on a friendship that began with a fan letter

and took him far from the big stadiums and big money; ＂The Arm Talks,＂ on contemporary pitching strategy and the arrival of the split-finger delivery; and many others. Mr. Angell’s conversations with past and present players and managers, scouts and coaches, rookies and Hall of Famers enhance his o

wn expertise and critical appreciation, which define him as the game’s most useful and ardent fan. ＂Angell resembles a pitcher with pinpoint control. As a chronicler of the game, he’s in a class with Ring Lardner and Red Smith.＂--Newsweek. ＂Angell’s perceptions are fresh, vivid, and uncannily accura

te.... Only a fan who cares this much could observe so carefully and write so eloquently.＂--San Francisco Chronicle. ＂A triumph of art and grace.＂--Chicago Tribune Book World. In the course of a well-lived century, he established himself as. . .baseball’s finest, fondest chronicler. --The New Yorker

What puts Once More Around the Park on the big-league shelf is, above all, language and, after that, respect for the individuality and awe for the professionalism of its characters. Mr. Angell makes baseball sound like an art form; he demonstrates that writing about it is an art form, too.＂ --New Y

ork Times

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自動化倉儲撒水特性分析

為了解決Shelf的問題，作者陳躍仁 這樣論述：

台灣網路購物興盛，為能有效率配置大量商品，物流業使用自動倉儲來進行貨品存放，發生火災時，其延燒速度均十分迅速。在自動倉儲中，自動撒水設備可在火災時第一時間有效滅火並侷限火勢，在無人化的工作環境中是消防單位與保險公司認為較可靠的滅火設備。國內的自動撒水設備主要參考日本法規之規範，為了方便官方審核，法規規範僅限以手算方式設計撒水系統，法規規範過於簡略，對於倉儲內貨架型式、貨架排數及貨品分類等均無較細緻之規定，難以對應實務面之需求。現今中美各國已結合設計理念及法規開發出電腦模擬軟體進行水力計算，而國內則仍僅限手算，與各國已有明顯差異。本研究比較國內、中國GB及美國NFPA法規分析國內自動倉儲案例，

以最低撒水密度值來看，國內規範明顯較其他2國低估，建議國內法規應增加適合國內實務現況倉儲內貨架型式、貨架排數、貨品分類及儲貨高度等分類，再依照分類繪製防護空間撒水密度及撒水頭間距等對照圖表，以期待設計之自動撒水系統符合儲物空間之滅火需求；並以水力計算軟體KYPIPE評估國內自動倉儲之自動撒水設備幫浦出水量以130L/min（K值＝114）之合理性，發現20個撒水頭系統尚能符合需求，24及30個系統均有不足之情形；以樹狀、環狀及網狀等3種配管模式模擬24個撒水頭放水，以網狀配管模式具有最高之滅火效能，搭配既設合法幫浦規格可以達到法定撒水密度，在不更改既設幫浦及水源情況下，對於既設倉儲提升撒水密度

提供了一個方法。

On the Peculiar Perseverance Required to Endure the Life of the Writer

為了解決Shelf的問題，作者 這樣論述：

Writing is, and always will be, an act defined by failure. The best plan is to just get used to it. Failure is a topic discussed in every creative writing department in the world, but this is the book every beginning writer should have on their shelf to prepare them--which is to say, to console them

in their misery. Less a guide to writing and more a guide to how to simply keep on going, On Failure: The Peculiar Perseverance Required to Endure the Life of a Writer describes the defining role played by rejection in literary endeavors and contemplates failure as the essence of the writer’s life.

Along with his own history of rejection, Marche offers an historical framework--from Ovid’s exile and Dostoevsky’s mock execution to more contemporary tribulations--through which to consider rejection, and addresses the impact of the widespread decline of humanism on the twenty-first century writin

g life. Stephen Marche is a novelist, essayist and cultural commentator. He is the author of half a dozen books, and has written opinion pieces and essays for The New Yorker, the New York Times, The Atlantic, Esquire, The Walrus, and many others. He lives in Toronto with his wife and children.

貝氏最佳化的小樣本採集函數學習

為了解決Shelf的問題，作者謝秉瑾 這樣論述：

貝氏最佳化 (Bayesian optimization, BO) 通常依賴於手工製作的採集函數 (acqui- sition function, AF) 來決定採集樣本點順序。然而已經廣泛觀察到，在不同類型的黑 盒函數 (black-box function) 下，在後悔 (regret) 方面表現最好的採集函數可能會有很 大差異。 設計一種能夠在各種黑盒函數中獲得最佳性能的採集函數仍然是一個挑戰。 本文目標在通過強化學習與少樣本學習來製作採集函數(few-shot acquisition function, FSAF)來應對這一挑戰。 具體來說，我們首先將採集函數的概念與 Q 函數 (Q

-function) 聯繫起來，並將深度 Q 網路 (DQN) 視為採集函數。 雖然將 DQN 和現有的小樣本 學習方法相結合是一個自然的想法，但我們發現這種直接組合由於嚴重的過度擬合(overfitting) 而表現不佳，這在 BO 中尤其重要，因為我們需要一個通用的採樣策略。 為了解決這個問題，我們提出了一個 DQN 的貝氏變體，它具有以下三個特徵: (i) 它 基於 Kullback-Leibler 正則化 (Kullback-Leibler regularization) 框架學習 Q 網絡的分佈(distribution) 作為採集函數這本質上提供了 BO 採樣所需的不確定性並減輕了

過度擬 合。 (ii) 對於貝氏 DQN 的先驗 (prior)，我們使用由現有被廣泛使用的採集函數誘導 學習的演示策略 (demonstration policy)，以獲得更好的訓練穩定性。 (iii) 在元 (meta) 級別，我們利用貝氏模型不可知元學習 (Bayesian model-agnostic meta-learning) 的元 損失 (meta loss) 作為 FSAF 的損失函數 (loss function)。 此外，通過適當設計 Q 網 路，FSAF 是通用的，因為它與輸入域的維度 (input dimension) 和基數 (cardinality) 無 關。通過廣

泛的實驗，我們驗證 FSAF 在各種合成和現實世界的測試函數上實現了與 最先進的基準相當或更好的表現。