Mac Pages的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列懶人包和總整理

Tcm Underground: 50 Must-See Films from the World of Classic Cult and Late-Night Cinema

為了解決Mac Pages的問題，作者de Chirico, Millie,Murry, Quatoyiah 這樣論述：

Based on the Turner Classic Movies series, TCM Underground is the movie-lover’s guide to 50 of the most campy, kitschy, shocking, and weirdly wonderful cult films you need to see.In the pages of this book, you’ll explore this unique order of films--primarily from the 1960s, ’70s, and ’80s--with i

nsightful reviews, behind-the-scenes stories, subgenre sidebars, and full-color and black-and-white photography throughout. Go along for the ride with new takes on crime films, including The Honeymoon Killers and The Harder They Come. Witness one-of-a-kind horror in Bill Gunn’s landmark vampire film

Ganja and Hess and Nobuhiko Obayashi’s infamous and indescribable Hausu. Absorb the boundary-pushing documentary-style trilogy The Decline of Western Civilization, which throws you into indelible moments in the punk and metal music scenes. And marvel at pure ’80s oddities like Mac and Me and The Ga

rbage Pail Kids. From Possession to Polyester and Beyond the Valley of the Dolls to Xanadu, no two films are alike in this compendium. Just sit back and prepare to be surprised, amused, and entertained by this celebration of the stars, filmmakers, and stories behind fifty of the most beguiling and u

nforgettable movies ever to hit the screen.

Mac Pages進入發燒排行的影片

數學形態學導出多參數持續同調之層狀結構

為了解決Mac Pages的問題，作者胡全燊 這樣論述：

Topological Data Analysis (TDA), a fast-growing research topic in applied topology, uses techniques in algebraic topology to capture features from data. Its importance has been discovered in many areas, such as medical image processing, molecular biology, machine learning, and pattern recognition.

Persistent homology (PH) is vital in topological data analysis that detects local changes in filtered topological spaces. It measures the robustness and significance of homological objects in spaces' deformation, such as connected components, loops, or higher dimensional voids. In Morse theory, filt

ered spaces for persistent homology usually rely on a single parameter, such as the sublevel set filtration of height functions. Recently, as a generalization of persistent homology, computational topologists began to be interested in multi-parameter persistent homology. Multi-parameter persistent h

omology (or multi-parameter persistence) is an algebraic structure established on a multi-parametrized network of topological spaces and has more fruitful geometric information than persistent homology. So far, finding methods to extract features in multi-parameter persistence is still an open and

concentrating topic in TDA. Also, examples of multi-parameter filtration are still rare and limited. The three principal contributions of this dissertation are as follows. First, we combined persistent homology features (persistence statistics and persistence curves) and machine learning models for

analyzing medical images. We found that adding topological information into machine learning models can improve recognition accuracy and stability. Second, unlike traditional construction for multi-parameter filtrations in Euclidean spaces, we propose a framework for constructing multi-parameter fi

ltrations from digital images through mathematical morphology and discrete geometry. Multi-parameter persistence derived from mathematical morphology is more efficient for computing and contains intuitive geometric attributes of objects, such as the sizes or robustness of local objects in digital im

ages. We involve these features to remove the salt and pepper noise in digital images as an application. Compared with current denoise algorithms, the proposed approach has a more stable accuracy and keeps the topological structures of original data. The third part of this dissertation focuses on us

ing sheaf theory to analyze the lifespans of objects in multi-parameter persistence. The multi-parameter persistence has a natural sheaf structure by equipping the Alexandrov topology on the based partially ordered set. This sheaf structure uncovers the gluing properties of local image regions in th

e multi-parameter filtration. We referred to these properties as a fingerprint of the filtration and applied them for the character recognition task. Finally, we propose using sheaf operators to define ultrametric norms on local spaces in multi-parameter persistence. Like persistence barcodes, this

metric provides finer geometric and topological quantities.

Exploring Apple Mac: Monterey Edition: The Illustrated, Practical Guide to Using MacOS

為了解決Mac Pages的問題，作者Wilson, Kevin 這樣論述：

We’ve all been there before, glaring at a computer screen with no idea what to do - don’t worry Exploring Apple Mac is here to help. Written by best-selling technology author, lecturer, and computer trainer Kevin Wilson, Exploring Apple Mac is packed with easy-to-follow instructions, photos, illu

strations, helpful tips, and video demos.Updated to cover the Monterey release of MacOS, Exploring Apple Mac will help you understand the fundamentals of your Mac. You’ll learn how to: Upgrade your Mac to Monterey and explore new featuresFind your way around the desktop, dock, finder, and launchpad

in MacOS MontereyMaster touch gestures, trackpads and the magic mouse to get around your MacUse Notification Centre, Handoff, Spaces, Control Center, Universal Control, and Universal ClipboardOrganising files and folders in FinderBrowse the web with the Safari Web Browser safely and efficiently in M

acOS MontereyKeep in touch with friends and family using email, iMessage, and FaceTime video chatSet important appointments with Calendar in MacOS MontereyKeep the people you correspond with in the Contacts appSet yourself reminders and ’to-do’ listsCheck the weatherRecord voice memosDownload countl

ess apps from the App Store in MacOS MontereyCatch up with your favourite podcasts and the latest newsRead ebooks on MacOS MontereyFind your way around with the Maps App, get directions, create guides, explore places in 3D, and use the new interactive globeStream music with Apple Music, buy tracks &

amp; albums from iTunes StoreStream TV programs & movies with the Apple TV AppGetting started with Pages, Keynote and Numbers on your MacUsing iCloud storage on your MacUse time machine to back up your dataGet to know Siri in MacOSUse the photos app to store and enhance your photos Create slides

hows, photo albums and use iMovie to edit your home movies and more...Finally, system updates and maintenance tips to help you keep your Mac running smoothly complete this invaluable user guide. You’ll want to keep this edition handy as you make your way around the new OS.We want to create the best

possible resource to help you, so if we’ve missed anything out then please get in touch using [email protected] and let us know. Thanks.

應用模型壓縮技術並使用28奈米CMOS製程的23-FPS Tiny-YOLOv4物件辨識CNN晶片系統

為了解決Mac Pages的問題，作者呂映承 這樣論述：

摘要...........................................................iAbstract......................................................iiAcknowledgements.............................................iiiContents......................................................ivList of Tables...............................

.................viList of Figures..............................................viiChapter 1 Introduction.......................................11.1 Object Detection...........................................11.2 Related Works..............................................21.2.1 Object De

tection CNN Models............................21.2.2 Optimization Techniques...............................101.2.3 Hardware Accelerators.................................161.3 Objectives................................................361.4 Thesis Organization..............................

.........37Chapter 2 Proposed System...................................382.1 Hardware Accelerator Implementation.......................382.2 Dataflow and FPS Estimation of the Hardware Accelerator...412.3 Proposed Object Detection CNN Model.......................432.4 Summary of the P

roposed System............................49Chapter 3 Experimental Results and Verification Results.....503.1 Implementation Environment................................503.2 System Implementation and Simulation......................513.3 Verification Environment..........................

........553.4 System Verification and Demonstration on FPGA.............553.5 Comparison to Related Works...............................57Chapter 4 Conclusion and Future Work..........................59Reference.....................................................60