Complete Travel Packing Checklist

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Complete Travel Packing Checklist Destination: ............................. Number of Days/Nights: ............................. Weather: ............................. Essentials: Toiletries: Clothing: Travel Documents: Toothbrush & Toothpaste Casual Tops Photo ID / Driver’s License Body Wash / Soap Dress Tops Passport / Visa Facewash T-shirts Boarding Passes Deodorant Jeans (printed or electronic) Eye drops / Contact Solution Casual Pants Conﬁrmation Receipts Shampoo & Conditioner Dress Pants (printed or electronic) (hotel, train, Hand / Body Lotion Shorts bus, rental car, event tickets) Dresses Emergency Docs Health & Beauty: Skirts (health insurance card, allergy list, Blazers & Suit Coats emergency contact) Basic Medications Ties & Pocket Squares Funds: (headache, allergy, stomach upset, Sweaters motion sickness, sleep aid) Outerwear (Coat/Jacket) Wallet Basic First Aid Activewear Credit Cards (band-aids, antibiotic ointment) Cash Vitamins Swimwear & Cover-Ups Pajamas & Loungewear Other: Sunscreen Shaving Items Underwear Cell Phone + Charger Hair Product Socks Keys (gel, mousse, cream, paste) Bras Glasses / Contacts Hair Tools Tights / Hosiery Rx Medication (blowdryer, straightener) Brush, Hair Ties, Bobby Pins Shoes: Personal Comfort: Makeup Perfume / Cologne Tennis Shoes Neck Pillow Feminine Care Items Dress Shoes / Heels Warm Layer (shawl, sweater) Tweezers Flats Warm Socks Q-tips, Tissues, Cotton Sandals Eye Mask Rounds Boots Headphones / Earplugs Nail Polish Specialty Book / Magazines (water shoes, cycling shoes, hiking Water Bottle boots) Snacks / Gum Accessories: Change of Clothes Accessories: Notes: Sunglasses ...................................................... Watch Electronics: ...................................................... Jewelry Laptop ...................................................... Belts iPad / Tablet ...................................................... Scarf E-reader ...................................................... Hat Camera ...................................................... Purses ...................................................... All Chargers Umbrella ...................................................... Adapters / Converters ...................................................... Made for savvy travelers by .

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500 Years of Filipina Fashion
500 YEARS OF FILIPINA FASHION 2020 Asian Pacific Heritage Month 500 Years of Filipina Fashion By: Charity Bagatsing Doyl Research for this presentation includes: The Boxer Codex Emma Helen Blair’s The Philippine Islands 1493-1898 William Henry Scott’s Barangay Philippine Folklife Museum Foundation The Henry Otley Beyer Library Collection Thank You Dean Cameron, DaShond Bedford and Spokane Public Library. They do not however on this account go naked, they wear well made collarless robes which reach the ankles and are of cotton bordered with colors. When they are mourning, these robes are white. They take off this robes in their houses, and in places where garments are unnecessary. But everywhere and always they are very attentive to cover their persons with great care and modesty. Wherein they are superior to other nations, especially to the Chinese.” Father Chirino - Relacion de las Islas Filipinas 1602 One of the earliest records of Philippine fashions comes from the 16th century manuscript known as the Boxer Codex. 15th century Filipinos were described to wear stylish and lavishly beaded clothing from only the best and most expensive materials available at that time. In 1591, the Chinese merchants sold over 200,000 robes of cotton and silk up and down the islands. This shopping spree caused such an alarm to the Spanish regime because chiefs and slaves wore the same extravagant silk and lavishly beaded outfits, making it impossible to judge their rank from their dress. Another concern was the exorbitant amount of money Filipinos spent on their clothes, which the colonizers maintained should go to the Spanish treasury instead of the pockets of the Chinese traders.
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BELT RAILWAY of CHICAGO Special Instructions
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Fashion Outfit Generation for E-Commerce Elaine M
Fashion Outfit Generation for E-commerce Elaine M. Bettaney Stephen R. Hardwick ASOS.com ASOS.com London, UK London, UK [email protected] [email protected] Odysseas Zisimopoulos Benjamin Paul Chamberlain ASOS.com ASOS.com London, UK London, UK [email protected] [email protected] ABSTRACT Combining items of clothing into an outfit is a major task in fashion retail. Recommending sets of items that are compatible with a particular seed item is useful for providing users with guidance and inspiration, but is currently a manual process that requires expert stylists and is therefore not scalable or easy to personalise. We use a multilayer neural network fed by visual and textual features to learn embeddings of items in a latent style space such that compatible items of different types are embedded close to one another. We train our model using the ASOS outfits dataset, which consists of a large number of outfits created by professional stylists and which we release to the research community. Our model shows strong performance in an offline outfit compatibility prediction task. We use our model to generate outfits and for the first time in this field perform an AB test, comparing our generated outfits to those produced by a baseline model which matches appropriate product types but uses no information on style. Users approved of outfits generated by our model 21% and 34% more frequently than those generated by the baseline model for womenswear and menswear respectively. KEYWORDS Representation learning, fashion, multi-modal deep learning 1 INTRODUCTION User needs based around outfits include answering questions such as "What trousers will go with this shirt?", "What can I wear to a Figure 1: An ASOS fashion product together with associated party?" or "Which items should I add to my wardrobe for summer?".
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Dictionary of United States Army Terms (Short Title: AD)
Army Regulation 310–25 Military Publications Dictionary of United States Army Terms (Short Title: AD) Headquarters Department of the Army Washington, DC 15 October 1983 UNCLASSIFIED SUMMARY of CHANGE AR 310–25 Dictionary of United States Army Terms (Short Title: AD) This change-- o Adds new terms and definitions. o Updates terms appearing in the former edition. o Deletes terms that are obsolete or those that appear in the DOD Dictionary of Military and Associated Terms, JCS Pub 1. This regulation supplements JCS Pub 1, so terms that appear in that publication are available for Army-wide use. Headquarters *Army Regulation 310–25 Department of the Army Washington, DC 15 October 1983 Effective 15 October 1986 Military Publications Dictionary of United States Army Terms (Short Title: AD) in JCS Pub 1. This revision updates the au- will destroy interim changes on their expira- thority on international standardization of ter- tion dates unless sooner superseded or re- m i n o l o g y a n d i n t r o d u c e s n e w a n d r e v i s e d scinded. terms in paragraph 10. S u g g e s t e d I m p r o v e m e n t s . T h e p r o p o - Applicability. This regulation applies to the nent agency of this regulation is the Assistant Active Army, the Army National Guard, and Chief of Staff for Information Management. the U.S. Army Reserve. It applies to all pro- Users are invited to send comments and sug- ponent agencies and users of Army publica- g e s t e d i m p r o v e m e n t s o n D A F o r m 2 0 2 8 tions.
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Hoisting & Rigging Fundamentals
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Mining Fashion Outfit Composition Using an End-To-End Deep Learning Approach on Set Data
1 Mining Fashion Outfit Composition Using An End-to-End Deep Learning Approach on Set Data Yuncheng Li, LiangLiang Cao, Jiang Zhu, Jiebo Luo, Fellow, IEEE Abstract—Composing fashion outfits involves deep under- standing of fashion standards while incorporating creativity for choosing multiple fashion items (e.g., Jewelry, Bag, Pants, Dress). In fashion websites, popular or high-quality fashion outfits are usually designed by fashion experts and followed by large audiences. In this paper, we propose a machine learning system to compose fashion outfits automatically. The core of the proposed automatic composition system is to score fashion outfit candidates based on the appearances and meta-data. We propose to leverage outfit popularity on fashion oriented websites to supervise the scoring component. The scoring component is a multi-modal multi-instance deep learning system that evaluates instance aesthetics and set compatibility simultaneously. In order to train and evaluate the proposed composition system, we have collected a large scale fashion outfit dataset with 195K outfits and 368K fashion items from Polyvore. Although the fashion outfit scoring and composition is rather challenging, we have achieved an AUC of 85% for the scoring component, and an accuracy of 77% for a constrained composition task. Fig. 1: An example showing the challenging of fashion outfit Index Terms—Multimedia computing, Multi-layer neural net- composition. Normally one would not pair a fancy dress (as in work, Big data applications, Multi-layer neural network the left) with the casual backpack (as in the bottom right) but once you add in the shoes (as in the top right), it completes I.
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Dress of the Oregon Trail Emigrants: 1843 to 1855 Maria Barbara Mcmartin Iowa State University
Iowa State University Capstones, Theses and Retrospective Theses and Dissertations Dissertations 1977 Dress of the Oregon Trail emigrants: 1843 to 1855 Maria Barbara McMartin Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/rtd Part of the American Material Culture Commons, Fashion Design Commons, Home Economics Commons, and the United States History Commons Recommended Citation McMartin, Maria Barbara, "Dress of the Oregon Trail emigrants: 1843 to 1855" (1977). Retrospective Theses and Dissertations. 16715. https://lib.dr.iastate.edu/rtd/16715 This Thesis is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Retrospective Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Dress of the Oregon Trail emigrants: 1843 to 1855 by Maria Barbara McMartin A Thesis Submitted to the Graduate Faculty in Partial Fulfillment of The Requirements for the Degree of MASTER OF SCIENCE Major: Textiles and Clothing Signatures have been redacted for privacy Iowa State University Ames, Iowa 1977 ii TABLE OF CONTENTS Page INTRODUCTION 1 FACTORS CONTRIBUTING TO THE MIGRATION TO OREGON 5 THE OREGON TRAIL 13 PREPARATIONS FOR THE JOURNEY TO OREGON 19 CLOTHING AND ACCESSORIES OF EMIGRATING FAMILIES 28 THE EFFECT OF TRAIL LIFE ON CLOTHING 57 CARING FOR CLOTHING ALONG THE TRAIL 61 CONCLUSIONS AND SUMMARY 65 SOURCES CITED 68 ADDITIONAL MANUSCRIPTS CONSULTED 73 ACKNOWLEDGMENTS 76 iii LIST OF FIGURES Page Figure 1. Model of Conestoga-type wagon on display in Oregon Historical Society exhibit 22 Figure 2.
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Victorian - Edwardian - WW I
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Spring / Summer 2020 Belvest.Com
BELVEST.COM SPRING / SUMMER 2020 You are moving fast. You have a train to catch, another airport to get to. Backpack slung over your back; nothing can slow you down. The city is moving fast and you don’t want to be left behind. Once you reach your destination, you live every moment to the fullest, savoring each and every second until you prepare to leave again. Like any man that makes the world his dwelling, never stopping in one place, you refute past rules and labels. You conform only to the unique rhythm and language of your life. You are curious, and love discovering anything new and exclusive, because in the act of pursuit lies the true joy of every journey. And it is when you eschew the ordinary that we welcome you with open arms into our new showroom: a treasure trove in the heart of Milan, where you can discover the Spring/Summer 2020 collection, designed to accompany you wherever you decide to roam. Technical and ultranatural fabrics, relaxed shapes and oversize details follow your every move like a second skin. Because style isn’t a set of rules to be followed, rather a set of lines to be crossed. Diverge from the beaten track, trace your own path and pen your own story. BELVEST 4 BELVEST 6 7 SPRING / SUMMER 2020 BELVEST 8 BELVEST 10 11 SPRING / SUMMER 2020 BELVEST 13 SPRING / SUMMER 2020 14BELVEST 14 15 SPRING / SUMMER 2020 BELVEST 16 17 SPRING / SUMMER 2020 BELVEST 18 19 SPRING / SUMMER 2020 BELVEST 20 22 23 SPRING / SUMMER 2020 BELVEST 24 25 SPRING / SUMMER 2020 BELVEST 26 BELVEST 28 BELVEST 30 31 SPRING / SUMMER 2020 BELVEST 32 33 SPRING / SUMMER 2020 BELVEST 34 35 SPRING / SUMMER 2020 BELVEST 36 37 SPRING / SUMMER 2020 2 T0021630001 Single-breasted wool and polyamide jacket.
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Amtrak Service Standards Manual for Train Service and On-Board Service Employees, Version 6, 2011
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Welcome to the Fashion Costume Gallery
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Dress Like an Internet Celebrity: Fashion Retrieval in Videos
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20) Dress like an Internet Celebrity: Fashion Retrieval in Videos Hongrui Zhao1 , Jin Yu2 , Yanan Li3 , Donghui Wang1∗ , Jie Liu2 , Hongxia Yang2 and Fei Wu1 1College of Computer Science and Technology, Zhejiang University 2Alibaba Group 3Institute of Artificial Intelligence, Zhejiang Lab fhrzhao, ynli, dhwang, [email protected], fkola.yu, sanshuai.lj, yang.yhxg.alibaba-inc.com Abstract (a) IFN LSTM Same/Different Nowadays, both online shopping and video shar- IFN LSTM Similarity IFN DT ing have grown exponentially. Although internet Select Manually IFN LSTM celebrities in videos are ideal exhibition for fash- Same/Different … … … ion corporations to sell their products, audiences (b) do not always know where to buy fashion prod- … … Similarity ucts in videos, which is a cross-domain problem Detect IFN IFN called video-to-shop. In this paper, we propose Detect FD a novel deep neural network, called Detect, Pick, IFN IFN Keyframe and Retrieval Network (DPRNet), to break the gap Detect … Similarity … between fashion products from videos and audi- … … ences. For the video side, we have modified the tra- Same/Different ditional object detector, which automatically picks out the best object proposals for every commod- Figure 1: (a) shows the previous video-to-shop pipeline, which ity in videos without duplication, to promote the manually extracts frames containing the corresponding clothing in performance of the video-to-shop task. For the videos as the clothing trajectories. Then, (a) doing DT (detection and tracking) on clothing trajectories. Finally, (a) formulating re- fashion retrieval side, a simple but effective multi- trieval as a multiple-to-single matching problem after feature con- task loss network obtains new state-of-the-art re- struction using IFN (image feature network) and LSTM.
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