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Growing Up Weightless

為了解決Weightless的問題，作者Ford, John M. 這樣論述：

Out of print for more than two decades, John M. Ford’s Growing Up Weightless is an award-winning classic of a ＂lost generation＂ of young people born on the human-colonized Moon.Matthias Ronay has grown up in the low gravity and great glass citadels of independent Luna--and in the considerable sha

dow of his father, a member of the council that governs Luna’s increasingly complex society. But Matt feels weighed down on the world where he was born, where there is no more need for exploration, for innovation, for radical ideas--and where his every movement can be tracked by his father on the in

fonets. Matt and five of his friends, equally brilliant and restless, have planned a secret adventure. They will trick the electronic sentinels, slip out of the city for a journey to Farside. Their passage into the expanse of perpetual night will change them in ways they never could have predicted..

.and bring Matt to the destiny for which he has yearned. With a new introduction by Francis Spufford, author of Red Plenty and Golden Hill.Tor Essentials presents new editions of science fiction and fantasy titles of proven merit and lasting value, each volume introduced by an appropriate literary f

igure.

Weightless進入發燒排行的影片

無形資產與無形負債對公司經營績效之影響

為了解決Weightless的問題，作者戴偉丞 這樣論述：

在智慧財產技術發展迅速的時代，無形資產已成為公司不可或缺的資產之一，更成為公司與公司間競爭優勢的重要原因之一。無形資產的重要性一直是會計研究人員關注議題之一，公司有無形資產相對就有無形負債，無形負債或許在財務報表上面我們無法窺見，可是卻是存在的。無形負債可能導致公司破產，對公司的影響程度同樣也是不可忽略的。因此，本研究以台灣上市（櫃） 公司作為研究對象，並排除其金融保險業、資料不齊全，以2013年至2020年共8年期間為研究對象，其主要目的為探討無形負債是否存在以及無形資產與無形負債對公司經營績效之關聯性。本研究使用普通最小平方法 (Ordinary Least Squares,OLS) 再

建立多元回歸分析來進行實證回歸分析。本研究衡量無形資產與無形負債的方式為隱藏價值法，即公司市場價值扣掉帳面價值後為正數，則為無形資產；反之，當公司市場價值扣掉帳面價值為負數，則為無形負債。實證結果顯示，在研究期間，台灣的上市(櫃) 公司存在無形負債，其中有無形負債的公司佔研究樣本之21%至41%，另有134間公司在研究期間每年皆有未列示於帳載的無形負債，而隱藏價值不會隨著股價變動而有所變動。有無形資產(隱藏價值為正) 的公司，在經營績效上明顯優於無形負債(隱藏價值為負) 的公司，無形資產對經營績效有顯著正向關係，而無形負債對經營績效有顯著負向關係。

Growing Up Weightless

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為了解決Weightless的問題，作者Ford, John M. 這樣論述：

奇異值分解、高階奇異值分解及奇異譜分析 應用於背景影像還原

為了解決Weightless的問題，作者黎德輝 這樣論述：

ABSTRACT ixLIST OF FIGURE xxviiiLIST OF TABLE xxxiABBREVIATION xxxii1. Introduction 11.1. Motivation 21.2. Application 31.3. Challenge 41.4. Review of the background initialization method 51.4.1. Method based on pixel 51.4.2. Method based on iterative model completion 61.4.3. Method ba

sed on low-rank/ sparse data separation 71.4.4. Method based on neural network 81.5. Background/ foreground separation problem 91.6. Contribution 101.7. Dissertation organization 101.8. Publication 112. Data preparation 132.1. Scene background initialization database 132.2. Spatio-temporal d

ata 252.3. Storing a video as spatio-temporal data 262.4. Evaluation metric 282.4.1. Average gray-level error 302.4.2. Percentage of error pixels 302.4.3. Peak signal-to-noise ratio 302.4.4. Structural similarity index 312.4.5. Feature similarity index for image quality assessment 313. Backg

round initialization based on singular value decomposition 333.1. Introduction 333.2. Mathematical background on singular value decomposition 343.3. Proposed method 363.4. Experiment result and discussion 423.5. Conclusion 514. Background initialization based on higher-order singular value dec

omposition 524.1. Introduction 524.2. Mathematical background on higher-order singular value decomposition 534.3. Proposed method 544.4. Experiment result and discussion 574.5. Conclusion 635. Background initialization based on singular spectrum analysis 655.1. Introduction 655.2. Mathematic

al background on singular spectrum analysis 665.3. Proposed method 695.4. Experiment result and discussion 745.5. Conclusion 826. Conclusion and future work 84REFERENCE 86BIOGRAPHY 94