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I am normally a sz 5.5 and read in some reviews that this ring runs a little big and to go down a size. I am considering ordering a sz 5 but not sure. What would you recommend?
Beautiful sparkly stone that has 'fire'. One of the prettiest I own in the clear stone collection I have. I highly recommend anyone buying it if you're thinking about it, you won't be disappointed.
NEXTE Jewelry Silvertone Princess CZ Bridal-inspired Solitaire Ring
- Details
- Shipping & Returns
- Reviews
- Solitaire Engagement ring features a triple-grade 'AAA' princess-cut stone with fire and shine like a natural gemstone
- Jewelry is crafted of principal alloy with 1-micron rhodium plating
- Bridal-inspired engagement ring is a bold and powerful piece of jewelry to add to your collection
- Band is E-dipped in anti-tarnish for long life
- Cutting and polishing of each stone is premium-set to specifications
- Cubic zirconia is hand-set in a prong mounting
- Gemstone weight is 2 carats
- Item weight is 4.4 grams
- Ring setting measures 8 mm wide x 8 mm long
- Band measures 3 mm wide
Arrives in an Italian suede pouch.
All carat weights and measurements are approximate and may vary slightly from the listed dimensions. Treatment code CR. See Gemstone Treatments for further information.
Click here for Ring Sizing Chart
This ring cannot be resized.
Features | |
Metal | Brass |
Stone | Cubic Zirconia |
Ring Style | Engagement Solitaire |
Diamond Cut | Princess |
Gemstone | Multi-Stone Zircon |
Pearl Type | Pearl-Other |
Stone Shape | Princess |
Band Width | 3-4 mm |
Diamond Grade | Other |
Jewelry Setting | Prong |
Metal Color | White |
Dimensions | 2.0 cts, 4.4 grams TW |
Materials | principle alloy, rhodium, cz stone |
Model Number | XOGR 1026 RH |
Customer Reviews
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Elegant! Beautiful sparkly stone that has 'fire'. One of the prettiest I own in the clear stone collection I have. I highly recommend anyone buying it if you're thinking about it, you won't be disappointed.
Read MoreSandra Z. Apr 12, 2015 - Helpful 0
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Beautiful and elegant I've always wanted a princess cut engagement ring. Now married with a family, I have other bills to consider so I bought this CZ. It's soooo beautiful! It's exactly what I imagined my dream ring to look like. It dazzles and is dainty. I love it, I wear it constantly!!!
Read Morevyworld Jun 26, 2014 - Helpful 0
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Not a good buy Pretty in the picture but looks very fake in person. You can't fool anyone with this!
Read Morechyna1006 Apr 18, 2014 - Helpful 0
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ENGAGED! I got this for my girlfriend (fiance now!) and she absolutely loves this ring! It was her dream ring and it is very beautiful and looks just as good if not better then the real thing! In these tough economical times a ring like this is perfect to show your love, but also keep your wallet happy! We couldn't be happier with the ring, price, and how fast it got here! One day I'll buy her a real diamond ring, until then this gives her a ring she loves to wear and cherish with all her heart while allowing us to save money towards the wedding and reception instead of breaking the bank and having to spend even more money! Thank you Overstock.com for this beautiful ring and the opportunity to get engaged faster!!! I'M SO IN LOVE!!!!!!!
Read Morefrankmaneyack Nov 25, 2013 - Helpful 0
- Not Helpful 0
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Do not buy This ring was very pretty upon receiving it. Shipping was very fast. I returned the ring. It started turning my finger green after two days of wear.
Read Morecdh30702 Aug 2, 2013 - Helpful 0
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Amazing!! I took my time and read the reviews of lots of rings.. the reviews on this one helped me make my decision, along with the picture and discription. I usually like wearing the real thing, so I am very picky when purchasing faux or costume jewelry. I like quality. So for me to say this ring is exceptional, is saying a lot!The ring is actually even more beautiful than I imagined. I wanted a princess cut to wear to outings, etc.I currently have a beautiful 1 carat marque (sp) real diamond ring, but wanted to have a princess cut too.. this one can definately pass as the real thing! I will probably order another one to keep as a spare.. I plan to wear this one a lot. If you are thinking about getting this ring, don't hesitate, its a beauty and definately doesn't look like a fake, as some of the other imitation diamond rings can sometimes look too big, or unbelievable.. This one is georgous! lots of colorful shine and sparkle. HIGHLY Recommend!!! If you like large stones and wear them anyway, as I do, your friends will think you got a new diamond!!! LOVE THIS RING!!!!
Read Morekj69 Oct 25, 2010 - Helpful 7
- Not Helpful 0
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AWESOME!!! I got this for my fiance as a "back up" for when we go on vacation and out. She absolutely LOVES it!! It looks just like her real engagement ring but.... even better!! It looks SO REAL... and she gets complements all the time.
Read Morerileyshay11 Feb 2, 2011 - Helpful 3
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So Beautiful!!! I ordered this ring and it was delivered quickly. It is beautiful. A lot of facets....the light just flies off it. It looks entirely believable. I haven't had any problems with the band and have been wearing it for about 3 days now. I do not shower or do dishes with it on though. It can pass for the real thing if any of you ladies/men out there are wondering. It is large but the shine and quality of the stone are superb...if someone didn't know whether it was real or not, they would not know the difference. I love this ring.
Read Moretiffanyj27 Nov 11, 2010 - Helpful 3
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Great.. I mean, really. This for $$$? I am not an expert on Diamond but one thing I have to say regarding this Ring, is so "REAL" looking!!! I just love it!! I really do. If you like a Large Stone in the middle, well this is the perfect ring for you. You are going to love it, I guaranted it.
Read Morehibrea Mar 21, 2011 - Helpful 2
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OMG This is the 2nd cz ring that i have purchased from Overstock. This ring is stunning and I didnt think any ring could top the first one i bought. The day I received it I was shocked. I got sooooo many compliments on this ring even to the point I had women ask me how expensive it must of been and I am one lucky lady to have a man who loves me that much. I just smile and say thank you. Just has to be my little secret. Please, if anyone is thinking about buying this DO! You wont be disappointed. I wouldnt hesitate to use as an engagement ring. With the economy being as it is you can wear this ring with pride and joy. Thank You Overstock
Read Moredebbiehorney Sep 25, 2010 - Helpful 2
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Questions & Answers
We would suggest that you order the ring true to size, as the ring cannot be resized.
compared to carat weught in diamonds how big is the cz?
Gemstone weight is 2 carats.
Shopping Guides
What to Wear with a Cubic Zirconia Cocktail Ring
Right now one of the trendiest pieces of jewelry is the cocktail ring. Cubic zirconia cocktail rings allow women to indulge in extravagant rings without emptying their bank accounts. Cubic zirconia rings are available in a range of styles and each is very durable. High-quality cubic zirconia cocktail rings can be found in every price range. You can choose from cubic zirconia rings with sparkling clear stones or colorful gems set in sterling silver, white gold, yellow gold or gold-plated silver. Now that you are aware of the many stylish options available in cubic zirconia jewelry, use these tips to fashionably pair cocktail rings with the clothing and jewelry you already own.
Top 5 Cubic Zirconia Rings
Owning even just one shiny, glittering ring gives you a whole new world of style options. Sparkly rings are essential when you are dressing up for a party, but they look just as fabulous with your favorite pair of worn jeans. You don't have to splurge on diamonds to add shimmery jewelry to your collection. Cubic zirconia rings can be just as lovely as rings embellished with more expensive gemstones. Cubic zirconias look just like diamonds when viewed with the naked eye, but they are grown in a lab. This makes cubic zirconia rings much more affordable than other types of gemstone jewelry. A wide selection of cubic zirconia jewelry awaits you at Overstock.com. Allow us to narrow down the choices with a few of our favorite pieces of fashion jewelry.
Tips on Wearing Cubic Zirconia Rings
If you're looking for sparkle on a budget, consider cubic zirconia rings. Cubic zirconias are beautiful gemstones that show well in all types of jewelry. Sometimes abbreviated as CZ, a cubic zirconia is a synthetic diamond gemstone. Cubic zirconia jewelry is popular for its affordability, flawless appearance and versatility. These rings are lovely and stunning and come in many varieties, including stackable rings, cocktail rings, engagement rings, promise rings and big, beautiful solitaires.
Top 5 Settings for Cubic Zirconia Rings
Inject more sparkle into your life with cubic zirconia rings. CZ rings are gorgeous and durable, yet they are more affordable than diamond and gemstone rings. Cubic zirconias look just like diamonds; it takes a trained gemologist to tell the difference between the two stones. Cubic zirconia rings make beautiful and budget-friendly engagement rings, as well as stunning fashion jewelry. You can buy cubic zirconia jewelry for yourself or for a loved one, but before you shop, consider these tips on CZ ring settings.
Top 5 Styles of Cubic Zirconia Engagement Rings
When you are shopping for engagement rings, you will find many featuring stunning cubic zirconias. Choosing a cubic zirconia engagement ring can give you the style you really want, even if you can't afford the same style in the diamond ring category. Cubic zirconia rings are built to last, especially if you select one with a platinum or gold setting. Whether as a temporary, affordable engagement ring or as an eco-friendly investment ring, cubic zirconia rings make stylish choices.
Best Reasons to Shop for Cubic Zirconia Engagement Rings
Cubic zirconia engagement rings are becoming a popular wedding jewelry choice. Cubic zirconias, also called CZs, are synthetic gemstones. The most popular of lab-created diamond alternatives, CZs look just like diamonds to the naked eye. Cubic zirconia engagement rings are beautiful, ethical, and affordable. Consider all of these reasons to shop for CZ engagement rings when you are looking at wedding jewelry.
How to Care for Cubic Zirconia Jewelry
Cubic zirconias are beautiful and brilliant, making them well-suited for use in jewelry. They are also affordable diamond alternatives because they are created in a lab and, in many cases, must be tested in a lab to determine whether a stone is a diamond or a cubic zirconia, also known as a CZ. Cubic zirconia jewelry is quite easy to care for and doesn't scratch easily, but there are a few things you can do to keep your cubic zirconia rings, bracelets, earrings, and necklaces looking their best.
AAA Cubic Zirconia Buying Guide
A cubic zirconia is a great way to get the sparkling look of a diamond with a noticeably smaller price tag. This beautiful stone has become a staple in everything from fashion jewelry to high-end rings and necklaces. Crafted with zirconium oxide in cubic form, cubic zirconia jewelry, also known as CZ, is the most popular diamond alternative because of its dazzling appearance and realistic, diamond-like shine. CZ is a timeless material for all types of jewelry that continues to improve with technological developments.
FAQs about Cubic Zirconia Jewelry
Cubic zirconia jewelry gives you the sparkle of gorgeous gems without the high price tag. But cubic zirconia jewelry is not simply cheap jewelry; it is lab-created, yet durable and beautiful. If you're not sure if CZ jewelry is for you, read these answers to the most frequently asked questions about it, so you can learn more about the sparkling synthetic gemstone and shop with confidence for the pieces that are perfect for your style and budget.
Best Cubic Zirconia Bracelet Styles
Luxury bracelets are true indulgences; wearing sparkling gemstones on your wrist is fantastically opulent. If you are looking for a great way to add luxurious, sparkling gems to your bracelet and jewelry collections without breaking the budget, consider shopping cubic zirconia bracelets. Cubic zirconias are lab-created diamond alternatives, and they look just like the real thing. In fact, cubic zirconia gems can be even better than diamonds because they have lab-perfected clarity and color. Cubic zirconia bracelets are available in many styles. You can find cubic zirconia jewelry that looks just like more expensive diamond bracelets or choose a piece of fashion jewelry that is unique. Add cubic zirconia jewelry to your jewelry collection with our list of the best pieces featuring this gemstone.
100 Facets CZ Buying Guide
Even though dazzling jewels have been around for centuries, a new discovery or innovation still comes along once in a while that truly captures our imagination. That's why we are excited to tell you all about 100-facet CZs, a new design in cubic zirconia jewelry. This collection showcases a unique gem cut just for cubic zirconia -- a cut with 100 facets per gem. The 100-facet CZ cut enhances the gem's brilliance and turns it into a magnificent jewel.
How to Buy a Birthstone Bracelet
Personalization is the driving force behind our daily need to accessorize. One of the best ways to personalize your look is with birthstone jewelry. A birthstone bracelet features your birth month's gemstone and can be layered with other jewelry or worn alone for a simple, classic look. You have many options when shopping for a birthstone bracelet. You can buy a bracelet with authentic gemstones or save money on jewelry featuring cubic zirconias. Many styles exist to suit your personal jewelry tastes. Use these tips to buy the perfect birthstone bracelet for your collection.
Choosing the Right Density for Your Memory Foam Mattress
A decade ago, choosing a memory foam mattress was easier because there were so few brands and options. However, as the prices of memory foam mattresses have dropped, more brands have begun producing memory foam bedding, and choosing the right mattress has become more challenging. However, one factor remains constant throughout the different brands: memory foam density. Memory foam density, expressed in pounds per cubic foot, makes a big difference in the softness, durability, support, and price of your mattress. Knowing a little about the different densities of memory foam mattresses can simplify the buying process and help you find the memory foam mattress that's right for you.
Best Reasons to Shop for 3-band Wedding Rings
The modern bride has many options when shopping for wedding rings. Among those options is the 3-band wedding ring. Stylish 3-band wedding rings allow you to indulge in three gorgeous sparkling rings. Luxurious 3-band rings are most commonly found in bridal sets because most are made to fit together in one stunning jewelry showpiece. If you're considering a 3-band ring for yourself or for your bride, then use these tips to narrow down your choices in bridal sets.
Choose Your Dream Engagement Ring Setting
When shopping for engagement rings, you will find many variations of the same classic solitaire ring. In today's modern world, unique and artistic engagement rings are widely available. Avoid the carbon-copy rings and select a unique engagement ring that fits your style. Many modern brides choose colored diamond engagement jewelry or unique engagement rings made of industrial metals. Shopping by ring setting is also a great way to find an unconventional engagement ring. These unique engagement ring settings will inspire you to select funky and fashionable wedding jewelry.
Best Settings for Bridal Rings
The custom of wearing bridal rings is steeped in history. It's no wonder that the tradition lives on when you see the wide variety of baubles available in wedding rings. Bridal sets remain popular for their stunning styles and straightforward wearing. When purchasing a bridal set, you get at least two rings in one design. One ring is meant to be the engagement ring and one ring is meant to be a wedding band. Some bridal ring sets include an extra band for even more sparkle and style. Classic settings are available in bridal rings, as are more unique options. Consider these favorites when shopping for wedding jewelry.
FAQs about Wedding Jewelry
Wedding jewelry is a big part of our wedding traditions. Engagement rings, wedding bands and bridal jewelry all add sparkle and shine to getting married. When you are planning a wedding, you have many wedding jewelry choices to make. Engagement rings and wedding rings are both integral to the process of marrying, and most brides and grooms like to add wedding accessories to their gowns and tuxedos on the big day. Whether you are looking for a simple set of matching wedding rings or you want to find the perfect bridal jewelry to wear with your gorgeous wedding ball gown, our tips about wedding jewelry is a great place to start.
Top 5 Reasons to Consider Bridal Sets
You're engaged! Now it's time to start the shopping process. Before committing to an engagement ring, consider shopping for bridal sets. Bridal sets offer many advantages over engagement rings and wedding bands that you purchase separately. Bridal sets have a distinct look and may even save you money when shopping for wedding jewelry.
How to Match Your Wedding Ring to Your Engagement Ring
Commemorate one of the most important days of your life with fashionable wedding jewelry. Wedding rings and engagement rings are available in a variety of styles, making the shopping process quite involved. You have a several options for making sure that your wedding ring matches your engagement ring in a sleek and modern way. Consider the metals and stones when shopping for a wedding band and don't forget about bridal sets. Use these tips to match your wedding band to your engagement ring.
Top 5 Eternity Band Styles
Impress your wife or girlfriend on your next big anniversary with an eternity band. An eternity band is an anniversary ring that signifies your eternal relationship with an unbroken circle of diamonds or gemstones. Eternity rings can be worn next to a wedding band or they can be the perfect right-hand diamond ring. Eternity rings also work well as wedding bands; some brides opt only for an eternity band, skipping an engagement ring entirely. You have many style options when shopping for anniversary rings and diamond jewelry. Use this guide to find the perfect eternity band for yourself or for the woman you love.
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By Request
By David Lanz
By Request composed by David Lanz. For Piano. Hal Leonard Piano Solo. New Age. SMP Level 5 (Intermediate). Instrumental solo book. 144 pages. Published by Hal Leonard (HL.306657).
ISBN 0634086707. New Age. 9x12 inches.
20 piano solos from this Narada recording artist: Cristofori's Dream * Day Star * Desert Rain * First Light * Green into Gold * Heartsounds * Rain Forest * Rosario * Star Gazer * White Sands * and more.
About Hal Leonard Piano Solo
Intermediate to advanced piano arrangements with no lyrics.
About SMP Level 5 (Intermediate)
Seventh chords and melodies in both hands. More difficult note reading with ledger lines above and below the staff.

subset: commoncrawl


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Two stories: the first is about a man breaking into a genetic database storing medical information on the Icelandic people. After losing his daughter to a hereditary disease, he then tries to trace his own roots. The second story is about a murder investigation of an old man that leads to unusual forensic evidence, uncovering secrets that have been kept in the genetic bloodline of the whole nation.
Based on the eponymous crime novel by Arnaldur Indridason.
international title: | Jar City |
original title: | Mýrin |
country: | Iceland, Germany, Denmark |
year: | 2006 |
genre: | fiction |
directed by: | Baltasar Kormákur |
release date: | IS 20/10/2006, NO 16/11/2007, FR 10/09/2008, UK 12/09/2008 |
screenplay: | Baltasar Kormákur |
cast: | Ingvar Eggert Sigurðsson, Atli Rafn Sigurðarson, Björn Hlynur Haraldsson, Ágústa Eva Erlendsdóttir |
cinematography by: | Bergsteinn Björgúlfsson |
film editing: | Elísabet Rónaldsdóttir |
art director: | Atli Geir Grétarsson |
costumes designer: | Harpa Einarsdóttir |
music: | Mugison |
producer: | Baltasar Kormákur, Agnes Johansen, Lilja Pálmadóttir, Reinhard Klooss, Oliver Huzly, Kim Magnusson |
production: | Blueeyes Productions, Bavaria Film, Nordisk Film Production A/S |
distributor: | Sena (IS), Memento Films (FR), The Works (UK) |


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" Brand: Birzman / Type: Water Bottle Cages "
* Prices may differ from that shown
Your dooyooMiles Miles
Birzman Bottle Cage. Water Bottle Cages. Birzman Bottle Cage - Stunning Birzman design rendered in a super durable and tough high polymer engineered plastic. This rigid and lightweight design ensures bottles cannot bounce out whilst svelte overall weight makes for negligible impact on ....


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Prestonwood Forest Nite of Lites
Facebook Page
work 281-897-8597Description:
For over 35 years, Prestonwood Forest residents have been going all out when it comes to Christmas lights. Houston locals and visitors alike come out in droves to see one of the best neighborhood light displays in the country. These dazzling holiday lights have been featured in local newspapers, on Houston television, and even nationally on Fox News. About 750 homes participate in the Nite of Lites festival in Prestonwood Forest each year. It's a competition by home, culdesac and block in a variety of categories so the participants really get into it. Check out their website for times, maps and other details.


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If so, here's an idea: rather than competing for existing jobs, why not create your own? Whether you want to work with an environmental group, green business or government, why wait for someone else to "give" you a job?
Here are some essential ideas, starting points and resources for making it happen!
First Steps | Environmental Group or Organization | Green Business / Self Employment | Sustainable Food, Gardening & Agriculture | Environmental Education | Government & Public Sector | More Help & Resources | Comments & Feedback
Follow us on Twitter @yagottabob


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Plant Guide Cactuses Ferns Flowers Fruits Grasses Herbs Medicinal Plants Miscellaneous Plants Mosses and Lichens Lichens Genus Cetraria Genus Cladonia Genus Parmelia< Parmelia Conspersa Lichen Parmelia Perlata Lichen Parmelia Physodes Lichen Parmelia Saxatilis Lichen Wrinkled Parmelia Lichen Genus Peltigera Genus Physcia Genus Stereocaulon Genus Sticta Genus Thelochistes Genus Umbilicaria Genus Usnea Liverworts or Hepatics Mosses Mushrooms Nuts Spices Trees Vegetables Plant Dictionary Useful Websites |
Plant Guide > Mosses and Lichens > Lichens > Genus Parmelia
Genus ParmeliaGenus PARMELIA, (Ach.) De. Not. Parmelias usually grow as horizontal mats, gray, blue-green, dark brown, or brown tinged with green. They are closely attached by black rhizoids to rocks and trees and are distinctly branched and lobed. If the thallus is torn across and viewed with a hand lens, the torn edge shows that the thallus is composed of a layer of long, loose, white fibres (the medullary layer) between thin but more substantial surface tissues (cortical layers). Over the surface of the mat are scattered flat or saucer-shaped disks (apothecia) generally brown with a thin margin. These are numerous toward the centre. The apothecia have suggested the generic name Parmelia from parma, a small round shield. Many species have also a powdery appearance due to the bursting of the surface to emit little bodies (soredia) which may grow into lichens. Parmelia Conspersa Lichen Parmelia Perlata Lichen Parmelia Physodes Lichen Parmelia Saxatilis Lichen Wrinkled Parmelia Lichen |
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Polish families in England: decisions about stay and return
White, A., 2011. Polish families in England: decisions about stay and return. In: Stenning, A. and Slowik, A., eds. Post-accession migration in Europe: a Polish case study. Kraków: Impuls, pp. 15-38.
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Item Type | Book Sections |
Creators | White, A. |
Editors | Stenning, A.and Slowik, A. |
Departments | Faculty of Humanities & Social Sciences > Politics Languages and International Studies |
Status | Published |
ID Code | 23126 |
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zakbrooke's Mom Answers Activity
feeling the baby kick at 11 weeks??
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For anchor Steve Bartelstein, his career has ridden highs and lows. Unfortunately, in recent years, those highs have been few and far between.
Bartelstein is out as morning co-anchor at WBBM-TV after only 10 months, a station spokeswoman confirmed to FishbowlNY.
His last day was Friday.
While it's not certain what led to Bartelstein's demise, he's no stranger to what some perceive as bizarre behavior.
In 2007, Bartelstein was axed from WABC/Channel 7 after sleeping through his scheduled newscast.
He resurfaced at WCBS/Channel 2 six months later. During that time, Bartelstein was diagnosed with testicular cancer. He left the station abruptly in 2009. He claimed that WCBS mailed his paycheck, which was needed for medical expenses, instead of picking it up as he did previously.
FishbowlNY attempted to reach Bartelstein for comment.
Sister site TVSpy has more on Bartelstein's departure.


text:
Scrabble word: CASSETTES
In which Scrabble dictionary does CASSETTES exist?
Definitions of CASSETTES in dictionaries:
- noun - a container that holds a magnetic tape used for recording or playing sound or video
- noun - a small case containing audiotape or videotape
There are 9 letters in CASSETTES: A C E E S S S T T
Scrabble words that can be created with an extra letter added to CASSETTES
All anagrams that could be made from letters of word CASSETTES plus a wildcard: CASSETTES?
Scrabble words that can be created with letters from word CASSETTES
9 letter words
8 letter words
7 letter words
6 letter words
5 letter words
4 letter words
3 letter words
2 letter words
Images for CASSETTES
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SCRABBLE is the registered trademark of Hasbro and J.W. Spear & Sons Limited. Our scrabble word finder and scrabble cheat word builder is not associated with the Scrabble brand - we merely provide help for players of the official Scrabble game. All intellectual property rights to the game are owned by respective owners in the U.S.A and Canada and the rest of the world. Anagrammer.com is not affiliated with Scrabble. This site is an educational tool and resource for Scrabble & Words With Friends players.


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hand-face coordination
hand-face coordination
Tue, 11/29/2011Yuko ... there's ... there's something on your face ...
Good news: Johnny Wander Volume 2 will be here on Monday! They'll be going up for sale some time before that, I hope - will keep you posted. Till then we'll be here biting our nails hoping everything goes off without a hitch.
Catch you Thursday!


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our Cherished Ancient Manor in the green heart of Shropshire...
Here you will find a special atmosphere (in the words of our visitors), and we'll care for you as our guests: seeing you're comfortably accommodated and well fed, enjoying produce from our farm, as well as our talented local food producers. In the evening enjoy a glass of wine by an open fire.
We can be found in the nurturing Shropshire countryside, where you can Escape to the Country on walks in our private woodland and farm or by the river banks exclusive to our guests: home to rare wildlife. Alternatively, find a bench in a walled garden. Take a self catering cottage or plan a celebration.
The manor of Soulton -- set in our own working farm and woodland -- has a long history: back through the hands of a Tudor Lord Mayor of London and a Saxon adversary of the Viking King Cnut, to the Bronze Age. We are dedicated stewards of this long heritage, and we are working hard to be more and more sustainable.
Travel Note
Please be advised that there is a short term diversion on the east-bound/Wem approach route to Soulton; more details here.
Current Highlights...
- Escape to the Country return again to Soulton
- Renovation of our Tudor Moot Hall is compleated
"such a special place"
Via Twitter
"Highly Commended 'Green Project of the year 2014' "
Sustainability Awards 2014
"individual enthusiasm about conservation and farming"
Oxford University Wildlife and Conservation Unit


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Ghosts and Time Travel
Are "ghosts" invisible time travelers?
Started by petermattson , Sep 03 2013 10:28 PM
ghosts timetravel
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Publisher's Description
+Jitbit HelpDesk is the customer support and knowledge-base software. Jitbit HelpDesk is a web based help desk solution ("ticket system"), designed to deliver outstanding customer service to your clients. It is easy to install, accessible from anywhere as a web application and simple to use. Jitbit HelpDesk tracks and handles your customers' issues and saves your technicians' time.
What's new in this version:
Version 8.4 fixes inputs on registration form.


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Matinicus Harborfront Rental
Bedrooms: | 3 |
Bathrooms: | 1.5 |
Sleeps | 6 |
Per week | $900 |
Per month | $3,600 |
Type: | Home |
Natalie Ames
Property Rates
Begins | Ends | Week Night |
Weekend Night |
Weekly | Monthly | Min Stay |
Jun 01 2015 | Jul 01 2015 | - | - | $900 | $3,600 | 1 Week |
: 1 p.m. : 10 a.m. | ||||||
Lodging tax: 7% | ||||||
Deposits: $650.00 | ||||||
Payment Options: Personal Check | ||||||
Other Fees: additional 7% maine State Tax |
Reviews for this vacation home rental in Matinicus Island, Maine
R Pirrie
Storrs CT
beautiful island, great house
August 26, 2012
We've rented a lot of vacation houses, but many of them are just a place to sleep with no particular charm. This house was truly lovely and inviting. It was just as nice staying in for the morning, hanging out in the kitchen reading and drinking coffee, admiring the views out the windows front and back. Don't get me wrong, though. You'll want to go outside--the island itself is beautiful, with two white sand beaches and a stunning profusion of butterflies and wildflowers. You are so far away from shore that light pollution is minimal; If you've never seen the milky way, this is a great place to see it. Be prepared, though--going to Matinicus isn't like going to Cape Cod. You really need to be self sufficient--there are a few galleries on the island and a seasonal bakery, but that's it. No ATMs, no mini golf, no grocery stores. You need to be good at entertaining yourself--but if you love the outdoors and appreciate solitude, this place is magic. (One small suggestion--if the brush were cut back a little bit more in back, the views of the Old Cove would be even better.) I sincerely hope it won't be long before we enjoy the hospitality of the Ames again.
Baltimore, MD
Beautiful views!
August 15, 2010
Lovely expansive water views surround this peaceful island home. Very comfortable and well equipped for our family vacation. We loved sitting with a book or a cup of coffee on the back lawn overlooking Old Cove. Gorgeous!
Summer 2007, Marvelous views, great house and fun island.


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Orbital Photography
Using Hasselblad cameras with tack-sharp Zeiss optics, the Gemini crews were pioneers of orbital photography. On Gemini 10, future Apollo 11 astronaut Mike Collins opened his hatch to space, stood up, and photographed a color plate extended on a rod to test the accuracy of color photos in space. Back on Earth, Collins told debriefers: "It made little sense to me to try to retrieve that plate. So I just flung it overboard."


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Page 1
Estimating Violation Risk for Fisheries Regulations
Hans Chalupsky1, Robert DeMarco2, Eduard H. Hovy3, Paul B. Kantor2, Alisa Mat-
lin2, Priyam Mitra2, Birnur Ozbas2, Fred S.Roberts2, James Wojtowicz2, Minge Xie2
1 USC Information Sciences Institute, CA, USA
2 Rutgers, the State University of New Jersey, NJ, USA
3 Carnegie Mellon University, PA, USA
Acknowledgements. This report was made possible by a grant from the U.S. Coast
Guard District 1 Fisheries Law Enforcement Division to Rutgers University. The
statements made herein are solely the responsibility of the authors.
We extend a special thanks to LCDR Ryan Hamel and LT Ryan Kowalske for
working with us on this project, for their support and patience throughout this process.
Thanks also to CCICADA researchers Andrew Philpot and William Strawderman.
Dedication. This paper is dedicated in memoriam to Dr. Tayfur Altiok. Without
his efforts and motivation this project would not have been possible.
Abstract. The United States sets fishing regulations to sustain healthy fish pop-
ulations. The overall goal of the research reported on here is to increase the ef-
ficiency of the United States Coast Guard (USCG) when boarding commercial
fishing vessels to ensure compliance with those regulations. We discuss scoring
rules that indicate whether a given vessel might be in violation of the regula-
tions, depend on knowledge learned from historical data, and support the deci-
sion to board and inspect. We present a case study from work done in collabo-
ration with USCG District 1 (HQ in Boston).
Keywords: Regulatory compliance, Coast Guard, Fisheries, Machine learning,
Statistical models
This paper describes a targeted risk-based approach to enforcing fisheries laws in the
United States Coast Guard First District 1 (USCG D1), based in Boston, Massachu-
setts. The work is a joint project of the Laboratory for Port Security (based at Rutgers
University) and the Command, Control and Interoperability Center for Advanced
Data Analysis (CCICADA, a US nationwide consortium headed by Rutgers).
Fisheries rules and regulations have been established through a complex process
whose key aims include preservation of the fisheries biomass. The primary mission of
the fisheries law enforcement program is to maintain a balanced playing field among
industry participants (professional fishing companies) through effective enforcement
of the regulations. Over the years USCG D1 has developed an approach to fisheries
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law enforcement, which among other things includes scheduling fishing vessel in-
spections using a scoring matrix. In this paper we describe a project aimed at validat-
ing and extending the scoring matrix by further refining the ability to determine the
risk target profile of active vessels within the population of the First District.
Our research seeks a model that determines which vessels pose a higher safety risk
through non-compliance with safety codes and which vessels are most likely to be
contravening fishing laws and regulations. The main measure of effectiveness ex-
plored here, "boarding efficiency" (BE), is defined as the fraction of recommended
boardings that yield either a fishery or a safety violation. We also formulate other
measures of effectiveness and study approaches to improving them.
Currently the USCG determines whether to board a fishing vessel using a rule
called OPTIDE (created by LCDR Ryan Hamel and LT Ryan Kowalske of USCG
D1), which constructs a score by assigning points to known factors describing a ves-
sel, such as the time since last boarding and the vessel's history of fisheries violations.
The OPTIDE system recommends boarding if the sum of points exceeds a threshold.
The developers of the method used expert opinion to select the factors in the rule, and
to set their relative weights. The scoring matrix was developed using expert
knowledge. This paper addresses the question: Can naïve researchers using methods
of data analysis approach the effectiveness of such expert rules?
The USCG made available 11 years of data on USCG boarding activities and vio-
lations incurred by commercial fishing vessels. Our project studied introducing other
features, such as weather, seasonality, fish price, fish migration, key fish species,
home port, and detailed vessel history. The project team worked with economic data
such as fish market prices and considered socio-economic factors such as family fish-
ing boats in comparison to large commercial fishing vessels and fishers' attitudes
toward law enforcement. We looked at the seasonal variation in boardings and out-
comes. In the analysis, fisheries violations were separated from safety violations.
Machine learning methods were used to seek other features, or combinations of
present and added features, that might lead to decision rules increasing the BE. In
addition, alternative models for the boarding decision were considered. One model
poses a choice of which boat to board, within a set of K alternatives. Section 2 de-
scribes this approach. Another approach sought regression models that derive alterna-
tive weights for the same features used in OPTIDE. This method is discussed in detail
in Section 3. Section 4 discusses alternative goals, including balanced deterrence,
balanced policing, and balanced maintenance of safe operations. Here we discuss
alternative measures of effectiveness, e.g., violations found per hour rather than per
boarding. We also discuss alternative decision strategies: random strategies; varying
the number of boats used based on weather, season, or economics; alternative search-
ing protocols to find the candidate vessels for boarding.
RIPTIDE: A Machine Learning Approach
In this section we describe a scoring rule, RIPTIDE, which loosely stands for Rule
Induction OPTIDE. RIPTIDE extends OPTIDE by learning a more fine-grained and
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data-driven prediction and ranking model from past activity data, using a machine
learning approach. Using the best model found so far, RIPTIDE outperforms OPTIDE
by up to 75% with regard to a specific scoring rule, described in more detail below. A
software package implementing RIPTIDE can be used to experiment with the learned
models, and can be applied to rank operational data.
The OPTIDE rule was built based on expert judgment and intuition. It is an ab-
straction of a set of features that a commanding officer will routinely consider when
deciding whether to board a vessel. However, to our knowledge, there had been little
or no optimization of the rule based on historical data.
To extend OPTIDE, we used a data-driven machine learning approach to learn a
classification model from historic boarding activity data. RIPTIDE uses machine
learning to automatically find regularities in past boarding activity data and encodes
them in a model (or classifier) that can then be used to rank new, previously unseen
candidate boarding opportunities. The classifier takes a single (new) data instance and
applies the previously learned model to assign the new instance to one of two classes
(e.g., "violation" or "no violation"). In doing so, the classifier estimates a probability
that may be interpreted as the "confidence" of the prediction. This estimate is based
on how well the model performed for similar cases on the training data. These proba-
bilities can then be used to rank instances, as does the OPTIDE risk score.
Machine learning is built upon two core principles, data representation and gener-
alization. First, every data instance is represented in a computer-understandable form.
This is generally done by engineering a set of features or attribute/value pairs that
carry relevant information and that can be either directly observed or computed from
the data. In the generalization phase, the classifier uses many data instances for which
the class is known as training data, and seeks regularities in that data that allow it to
predict the class of a new data instance. There are many different data representation
schemes and learning algorithms that can be used (see, e.g., [2, 5, 9] for an overview).
For RIPTIDE, we chose a learning algorithm called a boosted decision tree that is a
good general-purpose tool for problems with a small to medium number of features.
One advantage of decision trees is that the learned models are (large) 'if-then-else'
statements that can be inspected by humans, and that are therefore to some extent
understandable. This is useful for comparison to a rule-based approach such as
OPTIDE, as the experts want to be able to decide whether they should trust such a
model. Other learning methods such as support vector machines or neural nets pro-
duce largely if not completely opaque models, which can be judged only by their
input/output behavior.
Classification performance can be improved by combining multiple classifiers that
were trained using different algorithms, features, sections of the data, etc. One such
strategy is called boosting. In boosting, instead of learning a single decision tree, we
learn multiple trees on different subsets of the training data. An algorithm such as
AdaBoost [4] (for Adaptive Boosting) then learns the "best" weights for combining
the results of those individual decision trees into an overall boosted decision tree. For
our currently best-performing classifier (Model 58), boosting improves performance
on a boarding tradeoff task (described below) by about 25%.
Page 4
Some 10,000 boarding activities from 2002 to the end of 2011 were used as train-
ing data and a set of about 1000 boardings in 2012 was used as a held-out test set to
evaluate the models. To use a classifier such as RIPTIDE, one must set a threshold,
which we can estimate from the training data. If the estimated probability of finding a
violation is above the threshold, we recommend boarding a vessel; otherwise, not. Let
TP be the number of true positives, that is, cases where the score is above threshold,
and the boarding in fact found a violation; the remaining cases where the classifier
says "board" are the false positives FP. Standard measures of effectiveness (MOEs)
for classifiers are recall R (the percentage of vessels having some violation that are
flagged for boarding), precision P = TP/(TP+FP) measuring the fraction of true deci-
sions, and their harmonic mean, known as the F1 value: F1 = 2*P*R/(P+R). Picking
a low probability threshold will give high recall but low precision; conversely, a high
threshold will give high precision but low recall. Every choice represents a tradeoff
between TP and FP, and what is acceptable depends on external factors such as task
objectives and resources. Using a generic rule such as maximizing R or F1 value will
generally not give the best compromise in practical applications.
The best way to compare classifiers without setting a threshold is to plot ROC (Re-
ceiver Operating Characteristic) curves. An ROC curve shows the true positive rate
(or recall) plotted against the false-positive rate, that is the ratio of false predictions to
the number of non-violating vessels, for each possible threshold point. The curve
shows a tradeoff space showing how many more false positives one must accept to
get additional true positives.
We can use the area under the ROC curve to compare different classifiers; a higher
area under the curve generally means a better classifier. Figure 1 shows a comparison
of ROC curves for OPTIDE and Model 58 for the held-out test data covering the year
2012. Both models have more or less identical area under the curve (AUC) of about
0.65, This shows that they are doing better than random choice (the dotted line with
an AUC of 0.5), but not very much so, indicating that there is not a very strong signal
in the data to begin with. Model 58 is doing significantly better at picking up the
higher yield boardings (the bump at the beginning of the curve), but it loses that ad-
vantage towards lower-risk boardings. It also is much more fine-grained than
OPTIDE, a feature we will explore in more detail below.
In the current formulation of OPTIDE, for values of the score, the yield distribu-
tion is very flat, which can be seen in the long straight sections of the OPTIDE ROC
curve. About 84% of all boardings fall in a very narrow band of yield close to the
threshold level. This means a large number of ships are apparently indistinguishable.
Our analysis of the data suggests that there are no standout "red flags" that positively
indicate that a ship might be in violation of some regulation. Even among vessels
having the highest risk score, only one third of boardings yield a violation. This
means we cannot assign a strong meaning to any of the OPTIDE risk categories.
Instead of focusing on absolute risk scores with a global interpretation, we explore
an alternative MOE: How well can a model select among a small set of alternative
vessels? For example, a set of ships may be encountered more or less simultaneously,
calling for an informed decision as to which ships to board, given available time and
resources. Technically, this calls for ranking the boats in the small candidate set.
Page 5
Fig. 1. ROC curves for OPTIDE and Model 58 for the held-out test data in 2012. Model 58 is a
weighted combination of 20 different tree models, found using AdaBoost.
To evaluate ranking performance we consider the following MOE. Given a test set
of boarding activities such as the 2012 held-out set, we randomly pick a set (or buck-
et) of size k and rank the elements in the bucket according to our model. We then pick
the top-ranked boarding activity in the bucket (choosing randomly in case of ties) and
test whether it actually had a violation or not. We repeat this experiment many times
and compute the fraction of trials in which we picked a winner (i.e., a boarding with a
violation). The probability of picking a winner is strongly dependent on the bucket
size, since smaller buckets have a smaller chance of containing a vessel with a very
high score. For example, for the held-out set of 1002 boardings of which 14% yielded
a violation, the probability that a random set of two boardings contains at least one
with a violation is about 26%, for 5 it is 53%, for 10 it is 78% and for 20 it is 97%
(almost certain). Note that this high probability doesn't mean that it is easier to find
one with a violation; that aspect still requires a good ranking function to find the best
item in the bucket. Since all of our analysis is based on data collected under historical
boarding policies, and, more recently, OPTIDE, the practical implications of the find-
ings in this section remain to be explicated in future work, which our USCG partners
are currently undertaking in exploration of our new ideas.
Table 1 shows the results of these experiments. It compares our currently best
model, Model 58, to OPTIDE and two other models. Model 58 includes features not
used in OPTIDE, such as distance to coast and vessel subtype. An alternative model
(Model 57) omits a feature (distance to coast) and still a third model (Model 48) adds
something called observed activity as a feature. The top of Table 1 shows standard
AUC and Max-F1 metrics, and all models perform fairly similarly. In the lower por-
tion, we show results on ranking experiments with bucket sizes ranging from 2 to 50.
We find that our best model improves up to 76% over OPTIDE for a bucket size of
20, where we have an almost 45% chance to pick a winner, and even for a more real-
istic bucket size of 10, the improvement is still a good 38%. This shows that the ap-
parently small advantage of RIPTIDE at higher levels of yield can become a substan-
Page 6
tial improvement if it is possible to batch the candidate vessels and choose the most
likely one to board.
Table 1. Evaluation results for OPTIDE and several alternate models
Random OPTIDE Model
58 vs.
Bucket Size
15 135
Choose 1 of k
5 0.135 0.210 0.217 0.236 0.243 +15.9%
10 0.135 0.237 0.279 0.311 0.328 +38.5%
15 0.135 0.244 0.328 0.364 0.393 +60.9%
20 0.135 0.251 0.363 0.403 0.443 +76.4%
25 0.134 0.261 0.399 0.440 0.484 +85.1%
30 0.135 0.276 0.422 0.466 0.516 +86.8%
35 0.135 0.290 0.447 0.488 0.542 +86.6%
40 0.134 0.307 0.464 0.505 0.567 +84.7%
50 0.137 0.336 0.492 0.542 0.601 +78.9%
We have developed a small RIPTIDE software suite that can be used to classify
and rank potential boardings based on the best models found so far, and to retrain
models if necessary. RIPTIDE builds upon the Weka toolkit [5] and adds a number of
methods for data translation and various other tasks. RIPTIDE is purely Java based
and can be run on Linux, MacOS and Windows platforms
Using the RIPTIDE approach in practice will require the users to retrain the ma-
chine learning models at regular intervals, perhaps on a yearly basis, to ensure that
significant changes in behavior are incorporated. This would be an uncomplicated
task, as long as the basic set of features to consider remains the same or similar. The
actual implementation of RIPTIDE is experimentally underway at the USCG.
In this section, we describe an alternative approach that utilizes regression methods
in statistics and the historical data to derive alternative weights for the same features
used in OPTIDE. Based on this approach, a new decision rule was developed, called
Data-Enhanced OPTIDE (DE-OPTIDE). We compare its performance with the origi-
nal OPTIDE rule.
An underlying assumption of OPTIDE is that probability of a violation is related to
an underlying score that is a weighted sum of some predictor variables X1, X2, …., Xn
(i.e., features used in the OPTIDE rule). The decision is made to board if the score
Page 7
exceeds a threshold. This assumption, plus potential random errors, leads us directly
to a statistical model called a logistic regression model (see [6]). Logistic regression is
an instance of a generalized linear model [1, 8]. It allows one to analyze and predict a
discrete outcome (known as a response variable), such as group membership, from a
set of variables (known as predictor variables) that may be continuous, discrete, di-
chotomous, or a mix of any of these. Generally, the response variable is dichotomous,
such as presence/absence or success/failure. In our case the response variable is the
violation indicator (presence/absence) of a vessel.
When sample data from such a model are available, we can perform a statistical
analysis to estimate the unknown coefficients and thus estimate the relationship be-
tween the response and predictor variables. We can then use the logistic regression
model to predict the category to which new individual cases are likely to belong.
We assume a violation is related to an underlying latent score S which is a
weighted sum of some predictor variables plus potential errors, i.e., S = W1X1 + W2X2
+ … +WnXn + error, where the Ws are weights describing the contributions of the
feature and the random "error" follows a normal distribution with mean 0 and vari-
ance σ². As with the tree-based rules, if the score of a vessel exceeds a certain thresh-
old value, the vessel should be boarded. Mathematically, these assumptions lead to
the aforementioned logistic regression [3,10]. We used logistic regression and the data
set available to us to estimate the coefficients W1, W2, …, Wn and we then used these
weights to create a new decision rule. Since the new decision rule uses the same fea-
tures as in the original OPTIDE rule but their weights are determined by the historical
data, we call the new rule a Data-Enhanced OPTIDE (DE-OPTIDE) rule.
We note that in the original OPTIDE matrix, all of the features are categorical.
Although some of them are naturally continuous, they are categorized or binned for
the analysis, which may cause some loss of information. We therefore performed an
additional analysis using the same set of features, but retaining continuous values for
some of the features. Using the continuous versions does somewhat improve the per-
formance of the DE-OPTIDE rule. In treating the features as continuous, we em-
ployed standard imputation techniques for missing data.
In our analysis, we randomly split the entire boarding data set available to us into
two subsets: 50% used for training and 50% used for validating. We fit the logistic
regression model to the training data and used the estimated probabilities to determine
a new decision rule. Then we applied the new rule to the remaining 50% of data to
assess its effectiveness. In the new decision rule, the threshold for boarding was cho-
sen by either setting a required percentage of vessels to be boarded, or setting a target
boarding efficiency. To control variation caused by the random 50-50 splitting, the
calculations were repeated 10 times. Therefore, the results we describe do not corre-
spond to a single unique boarding rule.
Starting with just categorical data, we explored the relationship between the Board-
ing Efficiency BE and the percentage of recorded boardings (that is, the fraction of all
records in the data set for which boarding is recommended, at a given threshold).
Results are shown in Figure 2. When applied to the data that was not used to train the
model, DE-OPTIDE yields a somewhat higher or similar BE compared to OPTIDE
for almost the entire range of recorded boarding percentages. For DE-OPTIDE, effi-
Page 8
ciency ranges from 20% to 35%, and setting the threshold to reduce the number of
boardings yields higher efficiency. This is because the rule ranks vessels by their
probability of yielding violations. Therefore, when fewer are boarded, the average
chance of finding a violation is higher. In choosing the threshold for the decision rule
one may need to take into account not just efficiency but also the fraction of recorded
Fig. 2. Boarding Efficiency vs. percentage of recorded boardings using both OPTIDE and DE-
OPTIDE for different thresholds (test data 50%). The results are based on 10 repetitions of the
random selection of training data.
We also compared the efficiency of DE-OPTIDE with that of OPTIDE using an-
other MOE. The threshold for DE-OPTIDE was chosen based on examining the effi-
ciency of the procedure over different percentages of recorded boardings. We found
that efficiency for DE-OPTIDE with a decreasing percentage of recorded boardings
starts to increase when the percentage of recorded boardings is less than 10%. Thus,
we chose the threshold corresponding to 10% of recorded boardings for DE-OPTIDE.
We also explored an alternative way of selecting the threshold for OPTIDE, i.e.,
letting threshold correspond to 10% of the recorded boardings (RBs), as we did with
DE-OPTIDE. We found that the efficiency of the DE-OPTIDE procedure reaches
32%, compared to 24% efficiency of OPTIDE when using an adjusted threshold (due
to our data omitting values for some of the OPTIDE features) and 27% if we use
OPTIDE with threshold corresponding to 10% of RBs. We recognize that the USCG
would not cut boardings to one tenth of the current level. However, some combination
of this rule in a randomized or mixed strategy for boarding might be effective. Note
that selecting vessels for boarding purely at random yields only 16% efficiency.
Figure 3 presents the ROC curves for both the OPTIDE and DE-OPTIDE rules.
This plot helps to illustrate the performance of these two decision rules as the thresh-
old is varied over the entire range of possible values. The ROC curve for OPTIDE has
an area of 0.576 under the curve, while that for DE-OPTIDE has AUC = 0.605.
Again, this indicates that the DE-OPTIDE rule is somewhat better than the OPTIDE
rule. These plots are based on a single random selection of the training data. Plots
from nine other repetitions are similar.
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Fig. 3. The ROC curves for both the OPTIDE and DE-OPTIDE rule for various choices of
thresholds (test data =50%). The plots are each based on a single run. Plots for 9 other runs
show the points for DE-OPTIDE lying almost always above those for OPTIDE itself.
Next we used logistic regression treating certain features as continuous. We com-
puted the relationship of the BE to the percentage of recorded boardings under the
modified DE-OPTIDE rule using some continuous features, a rule we call DE-
OPTIDE-C. DE-OPTIDE-C achieves better efficiency than OPTIDE. For OPTIDE,
efficiency ranges from 20% to 30%. For DE-OPTIDE-C efficiency rises to almost
35% at levels below 10% of recorded boardings. As with the discussion of batching in
Section 2, it is not known whether the set of candidates could be expanded enough for
such a lower fraction of sightings to yield an acceptable number of boardings.
We also compared the efficiency of DE-OPTIDE-C to that of OPTIDE using alter-
native ways of setting the threshold. The efficiency of the DE-OPTIDE-C procedure
reaches 34%, compared to 32% for DE-OPTIDE.
Other Approaches
In this section we consider other MOEs, e.g., violations per hour of enforcement
activity rather than violations per boarding. We also mention alternative decision
strategies: random strategies; changing the number of patrol boats based on factors
such as weather, season, or economics; and varying the protocols for finding
candidates for boarding.
Other Ways of Measuring Effectiveness
The models discussed so far consider all violations to be equally important. From the
perspective of deterrence, this is plausible. But in terms of economic impact on
fisheries and lives saved it may be more appropriate to group violations into classes
i=1,2,…,.I and seek to maximize the sum Σwixi where xi is the number of violations in
class i. For this to be meaningful the weights must be defined on an interval or ratio
scale, and not be simply ordinal [12,13].
Page 10
The "denominator" in the MOE has been "boardings." Alternatively, we may want
to measure effectiveness against time. Time is spent both in boarding and in seeking
the next candidate. The choice of which to use will lead to different decisions.
Suppose (based on the scoring rule) Vessel A has estimated 12% yield (probability a
violation will be found) and the predicted time for the boarding is 4 hours. Vessel B
has 15% yield and predicted boarding time 6 hours. If efficiency is violations per
boarding (VPB), Vessel A has 0.12 VPB, and Vessel B has 0.15 VPB. We prefer to
board Vessel B. If efficiency is violations per hour (VPH), then Vessel A has
0.12/4=0.03 VPH, and Vessel B has 0.15/6=.025 VPH. So we prefer to board Vessel
A. In fact, boarding time varies randomly, according to some rule that could be
estimated from data. One might also include in the denominator time spent seeking
the next candidate.
Other Kinds of Enforcement Strategies
The OPTIDE-class rules discussed here are deterministic. Randomized strategies
make it harder for intentional violators. The variation in goals discussed in Section 4.1
might be incorporated into a randomized mixture: e.g. 30% of time use OPTIDE, 40%
of time use VPB, and 30% of time use VPH.
We can model the boarding decision as a choice between boarding and seeking
further targets. For simplicity we suppose that a patrol boat meets a fishing vessel
every T minutes, and must immediately decide whether to board it. That the decision
to board must be made immediately is based on observations from [7] that fishermen
can and do modify their behavior when they observe Coast Guard boats, seeking to
limit the violations found if boarded. One boat every T minutes is a simplifying model
of the random rate at which a patrol will encounter fishing vessels.
Suppose the yield p varies uniformly from 0 to 1. Suppose boarding takes time tT.
What value of p should be the threshold for boarding? It can be shown that under
certain assumptions, the optimal choice is
(2 2)t
As boarding time tT increases, the threshold yield p increases. This confirms the
intuition that the longer boarding takes, the pickier one must be in boarding. More
realistic models for T,t, and the distribution of p can be developed from log data.
Finally, we considered patrol strategies, using analogies to ecology where the
limiting resource is the energy available to predators [11]. In particular, we have
compared pure pursuers and pure searchers. The former expend little or no energy in
seeking food; they wait until sufficiently valuable prey (sufficiently risky vessel) is in
sight and then act (e.g. anolis lizards). Pure searchers (e.g., warblers) spend time and
energy prowling to seek food; when they sight it they decide whether to try to catch it
and in that case spend little time on pursuit. We studied when a pure searcher should
adopt the patient strategy of waiting for the "best" type of food (vessel with highest
Page 11
risk score) or the impatient strategy of waiting for a while for the "best" type of food
and then choosing what is available.
Bringing in Other Goals of Fisheries Law Enforcement
In addition to efficiency of boardings, fisheries law enforcement seeks other goals:
balanced deterrence, balanced policing, and balanced maintenance of safe operations.
To balance deterrence, the USCG might seek to board all vessels at least once a year.
This would require, at times, boarding a low yield vessel. When should this be done?
Should the rule depend on recent prior boardings? Suppose Vessel A has an estimated
yield of 13% and has been boarded twice in the past year while Vessel B has a 15%
yield and has been boarded six times in the past year. In some cases we might prefer
to board A rather than B. We might want to board neither, and wait for some boat that
has not been examined in two years.
We have developed a simple model representing a tradeoff between balance and
yield. The score is based on three parameters, y(v) = the yield assigned to Vessel v,
D(v) = days since Vessel v was last boarded, and α, a model parameter. The modified
score is S(v) = y(v) + αD(v). The probability y(v) depends on an initial class probabil-
ity for that boat and on its boarding history. The class probability reflects differences
that affect the probability of violation. Explicitly, we take y for a vessel with b past
boardings and u "successful" past boardings to be y = f(b,u) + .05Z where Z is uni-
formly distributed between −1 and 1, and f(b,u) is presumed to come from observed
We ran simulations of this model, with five candidates per day, selected uniformly
at random from the 100 vessels having the highest score at the start of the day. We do
not simply take the five with highest scores because they might not all be accessible:
the patrol might stay in a particular area and not all boats are fishing each day.
Running the model 20 times for 1095 simulated days (3 years), and for each α
between .0001 and .001 (incrementing α by .0001), we found the average output. A
scatter plot comparing average number of observed violations over the entire 3-year
period to average number of vessels boarded in the last year of the simulation can
offer predictions on what the outcome might be under different scoring rubrics. Future
work will consider more general scoring metrics.
Our analysis supports several conclusions. First, the existing OPTIDE approach ex-
tracts a nearly optimal rule based on the data that are used in it. The ROC curves pro-
duced by state of the art techniques for learning rules are somewhat above the curve
for the existing OPTIDE rule. If the number of vessels considered could be increased,
operation at a higher threshold for boarding would likely result in discovering a larger
absolute number of violations per year, contributing to both fishery management and
safety goals. Second, automated methods, as described in this paper, can be used to
extract optimal rules by analysts who have no subject area expertise in this domain.
Page 12
Indeed, such methods can find decision rules that perform as well as, or somewhat
better than, models that require substantial knowledge of the data and domain exper-
tise to develop. This means that as the USCG considers adding additional variables to
the rules that trigger boardings, the automated methods used here can assess, in ad-
vance, the effectiveness of using that additional data. All that is required is to develop
a data set in which the values of those new variables are reported along with the exist-
ing key variables and the results of the boarding. Finally, we have identified ways in
which the objectives of the scoring rule work can be made more complex and closer
to the operational realities of the USCG. Preliminary theoretical work has produced
simple models showing how to include those realities in the computation of the more
sophisticated yield representing complex goals of fisheries law enforcement.
We presented the results described here to USCG D1 in a briefing to the highest-
level Coast Guard leadership. The results were very well received and are in the pro-
cess of being implemented in USCG D1. In addition, the USCG Research and Devel-
opment Center is working with D1 to explore modifications in the methods that would
make them applicable to other Coast Guard districts around the country.
1. Agresti, A.: Categorical Data Analysis. Wiley-Interscience, New York (2002)
2. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2007)
3. Finney, D.J.: Probit Analysis (3rd edition). Cambridge University Press, Cambridge, UK,
4. Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Thirteenth In-
ternational Conference on Machine Learning, 148-156, San Francisco (1996)
5. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA
data mining software: An update. SIGKDD Explorations, Vol. 11, Issue 1 (2009)
6. Hilbe, J. M.: Logistic Regression Models. Chapman & Hall/CRC Press, London (2009)
7. King, D.M., Porter, R.D., Price, E.W.: Reassessing the value of U.S. Coast Guard at-sea
fishery enforcement. Ocean Development & International Law, vol. 40, pp. 350-372. Tay-
lor and Francis, London (2009)
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Hall, London (1989)
9. Mitchell, T.: Machine Learning. McGraw Hill, New York (1997)
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11. Roberts, F.S., Marcus-Roberts, H.: Efficiency of energy use in obtaining food II: Animals.
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671. Elsevier, Amsterdam (1994)
13. Roberts, F.S.: Measurement Theory, with Applications to Decisionmaking, Utility, and the
Social Sciences. Cambridge University Press, Cambridge, UK (2009)


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CENTRAL FALLS — Although he acknowledges that serving on the City Council will be "a little bit different this time," Bob Ferri says he is pleased and honored to be once again representing the residents of Ward 2 from a position in city government.
Ferri, 65, of Cottage Street, was the winner of Tuesday's special election to fill the Ward 2 council seat that had been held by Richard Aubin. He will return to the five-member City Council for the remainder of Aubin's four-year-term, which runs until November of 2013.
Read more in today's print edition.


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Teton Range > Grand Teton > Climber's Log
Grand Teton Climber's Log
[ Sign the Climber's Log ]
Michael Tilden | First Time | |
Upper Exum - first time climb - done in 1 day | ||
Posted Dec 29, 2014 1:38 pm |
larch32 | Very fun Date Climbed: Aug 17, 2014 | |
Owens Spalding. Hordes of people, terrifying afternoon storms, traffic jams outside Jackson Hole, and this was still one of the most enjoyable routes I've climbed. My partner and I debated for a while about what gear we'd need for the route, and finally opted to bring one 60m half rope, a tiny rack, rock shoes, harnesses, helmets... we ended up leaving the gear in our packs until the rappel. I would consider climbing again without a rope, but only if the weather were looking solid before the technical sections. Down-climbing wet granite sucks. If your reasonably fit, do yourself a favor and do it in a day. We had a handful of route finding issues but still climbed car to car in 12 hours with what felt like a solid but not out of breath pace and plenty of breaks. Also! there is water consistently along the route (at least in August there was) You could probably get away with a single Nalgene. | ||
Posted Oct 19, 2014 9:30 pm |
JRB | Didn't make it | |
Got off route on the central rib and ran out of daylight | ||
Posted Oct 19, 2014 8:38 am |
AjaxHiker | Icy & cold Date Climbed: Aug 20, 2014 | |
My wife & I climbed the P/G route with Jackson Hole Mountain Guides on a cold windy icy day. Made the summit, which was exhilarating, but will have to go back one day as we had no views to speak of from the top. JHMG is a great service! | ||
Posted Oct 16, 2014 4:17 pm |
BLong | Grand Day! Date Climbed: Aug 17, 2014 | |
After gale force winds at the Upper Saddle forced us to retreat on the 16th, we returned on the 17th to climb the peak via the Owens Spalding Route. The Grand is an incredible mountain and the OS is an excellent, direct and easy route. We started late the second day, leaving the trailhead at 10:00 AM. We summited at 5:00 PM and had the whole peak to ourselves. It was glorious redemption after the previous day's failure. | ||
Posted Sep 1, 2014 11:55 pm |
SawtoothSean | Best Weather Date Climbed: Aug 10, 2014 | |
Did Upper Exum route again. Best weather in 5 trips to the top. Met some great folks also. Saw Bear on the approach and had a great spot in the Meadows. People parachuting around the peaks and a glider plane also around. | ||
Posted Aug 13, 2014 7:51 pm |
dstevenson0204 | Upper Exum Date Climbed: Aug 3, 2014 | |
Big goal for me, finally accomplished. One day push from 11:45pm up to the summit via the Upper Exum Ridge by 10am. Sooo incredible! | ||
Posted Aug 4, 2014 7:07 pm |
nickmech | Upper Exum Date Climbed: Jul 17, 2012 | |
Wow! This was a big one for me. Great trip with Norman and Vlato. | ||
Posted May 1, 2014 1:43 pm |
climbermike85 | Exum Date Climbed: Aug 9, 1989 | |
Yes, climbed the lower and upper Exum (and other routes) to the summit. I love the Grand Teton. The next route to do is the North Ridge! | ||
Posted Apr 9, 2014 6:07 pm |
genkidave | O/S route with the boys Date Climbed: Aug 18, 2012 | |
Great day car climbing The Grand with my 2 sons, son in law and daughter's boyfriend. Did Owen Spaulding route. 4 summited and highest climb so far for #5. My second summit. Gotta love this peak! | ||
Posted Mar 26, 2014 11:07 pm |
nhberes | Upper Exum Date Climbed: Jun 27, 2013 | |
Beautiful day on the Grand. Perfect bluebird day and calm as could be on the summit. Definitely going back for more. | ||
Posted Mar 5, 2014 4:22 pm |
forgues9999 | Cascade Canyon trail | |
is beautiful in the fall. Saw three moose along the way | ||
Posted Nov 12, 2013 4:26 pm |
MtnMagic | Upper Exum Date Climbed: Sep 15, 2011 | |
All alone on a beautiful day. | ||
Posted Oct 7, 2013 4:50 pm |
Woodswalker | Grand Teton Date Climbed: Jun 20, 2012 | |
Cold with lots of rime. Owen-Spalding route. A great experience! | ||
Posted Sep 28, 2013 6:47 am |
montanachris | Amazing mountain Date Climbed: Aug 26, 2013 | |
Climbed as part of Summit for Someone for Big City Mountaineers. Took the PG route to Upper Exum and finished via the Horse. No views from top but we did marry some fellow climbers! Unforgettable!! | ||
Posted Sep 11, 2013 7:50 am |
merrill | OS Date Climbed: Aug 27, 2013 | |
The weather was supposed to be bad but held out until the second rappel when it started snowing. | ||
Posted Sep 2, 2013 12:11 am |
McCannster | via Upper Exum Date Climbed: Aug 23, 2013 | |
Soloed Upper Exum in prime conditions. Sunny and warm on top with the usual crowds. Weather came in on the way down. 15 hours car to car. | ||
Posted Aug 24, 2013 10:45 pm |
Alex Wood | Crowded Day Date Climbed: Jul 29, 2013 | |
Camped at the Moraines and had to hike up in a gnarly storm. We up early and got to the base of the route to discovery we weren't the only ones doing the Owens-Spaulding Route. There were about 40 other people on the route in front of us including a guiding group by Exum of 17. We didn't move fast at all and nearly froze to death. There was also at least two k Che's of fresh snow on everything so that made the going even harder. Climbed it all with gloves and approach shoes. We got down to the trailhead 6:30pm | ||
Posted Aug 24, 2013 10:31 am |
deathzonescience | Petzoldt Ridge | |
Climbed Petzoldt ridge route with Rob Hess of Jackson Hole Mountain Guides, probably the best guide for Grand Teton. Stayed at Corbet High Camp (11,000 ft)the night before and left at 4 AM on summit day. Summited at 11 AM. The climb is rated 5.7 III, but with climbing in approach shoes, pack and wearing gloves (finally warmed up enough to climb without gloves at about 9:30 AM) it was a fun challenge. Booked it down to the cars and arrived about 5 PM in parking lot. Petzoldt ridge is said to be the best route on Grand Teton. Surprisingly Ron and I only saw 1 other party on the route so it's a great route if you hate the crowds. | ||
Posted Aug 18, 2013 9:26 am |
Donno | Upper Exum Date Climbed: Jul 15, 1992 | |
14 pitches on Upper Exum after meeting George Knott and Rich on Wall Street. 1991: climbed to Enclosure with dad, Doug, Sully and guide. 2005: climbed Owen-Spalding with Chris & Reza | ||
Posted Aug 16, 2013 11:39 am |


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Ladies Football Team Prepare for Season at FIU Stadium There's nothing more sexy than a lady who likes football. Well maybe a lady who dons lingerie and plays football. And plays to win. The aptly named Miami Caliente held its first tryout on Friday in preparation for the 2010 season. More than 70 women attended the tryouts in the hope of earning a spot on the team. Less than 10 were eventually chosen. And that's just the initial cuts. These women not only have to be able to hit hard and run fast and be able to catch a ball on the fly, they need to look good doing it. "We look for three different things in our athletes," said Mitch Mortaza, founder of the Lingerie Football League, who was in Miami on Friday overlooking the talent. "First and foremost, you have to be beautiful. And we make no bones about it. We gotta be able to market you as a player and the league. "And second you have to be confident. You have to have an air of confidence to play in your underwear in front of millions of people on television. And lastly but most importantly, is athleticism." The ladies are moving to a new field Last year, the Caliente played in the Bank Atlantic Center in Sunrise. This year, they will be playing at Florida International University Stadium in west Miami-Dade where they held the tryouts. "I loved Bank Atlantic Center but the problem was we were pretty far removed from downtown Miami," Mortaza said. "We're trying to get closer and obviously being in a college campus should play really well for us. College is a sweet spot. We expect to have many college students in these end zones." The Caliente made the playoffs last year but were eliminated in the first round, finishing at 2-3. They expect to go all the way this year. Season begins October 15 The Caliente kicks off its season October 15 against the Philadelphia Passion. Tickets went on sale today. Click here for more info on tickets and the team. Photos and video by Carlos Miller:
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Yang, Fan and Mao, Rihua and Zhang, Liyuan and Zhu, Ren-Yuan (2012) A Study on Radiation Damage in BGO and PWO-II Crystals. Journal of Physics: Conference Series, 404 . Art. No. 012025. ISSN 1742-6596. http://resolver.caltech.edu/CaltechAUTHORS:20130319-115145211
Full text is not posted in this repository. Consult Related URLs below.
Use this Persistent URL to link to this item: http://resolver.caltech.edu/CaltechAUTHORS:20130319-115145211
We report in this paper an investigation on radiation damage in 20 cm long BGO crystals produced at SIC as well as 20 cm long PWO-II crystals produced at BTCP for the proposed Panda experiment at GSI. The optical and scintillation properties of BGO and PWO-II crystals, such as transmittance, light output and light response uniformity, were measured before and after γ-ray irradiations at different dose rates. BGO crystals produced recently are compared to crystals used in the L3 experiment at LEP which was produced in early eighties. PWO-II crystals are compared to crystals used in the CMS experiment. Progresses are observed in the radiation hardness for both crystals. Their use for crystal calorimeters in future high energy physics experiments is discussed. To maintain crystal precision, a precision light monitoring system is mandatory to trace variations of crystal transparency in situ for a BGO or PWO crystal calorimeter in radiation environment.
Item Type: | Article | |||||||||
Related URLs: |
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Additional Information: | © 2013 IOP Publishing Ltd. The authors would like to thank Prof. Shaohua Wang of SIC and Prof. R. Novotny of the Giessen University for providing BGO and PWO-II samples respectively used in this study. This work was supported in part by the US Department of Energy Grant DE-FG03-92_ER40701. | |||||||||
Funders: |
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Classification Code: | PACS: 29.40.Mc; 61.80.Ed; 61.82.Ms; 29.40.Vj | |||||||||
Record Number: | CaltechAUTHORS:20130319-115145211 | |||||||||
Persistent URL: | http://resolver.caltech.edu/CaltechAUTHORS:20130319-115145211 | |||||||||
Official Citation: | A Study on Radiation Damage in BGO and PWO-II Crystals Fan Yang et al 2012 J. Phys.: Conf. Ser. 404 012025 | |||||||||
Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | |||||||||
ID Code: | 37561 | |||||||||
Collection: | CaltechAUTHORS | |||||||||
Deposited By: | Jason Perez | |||||||||
Deposited On: | 19 Mar 2013 23:17 | |||||||||
Last Modified: | 19 Mar 2013 23:17 |
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You could use coconut milk. Yummy.
Ah sorry I got nothing.
You could use coconut milk. Yummy.
Ah sorry I got nothing.
Stabbing conventional wisdom in its face.
Anyone who wants to talk nutrition should PM me!
i already drink coconut milk lol, use it in eggs, stir frys etc.... just found the stuff and lovin it
I'll play around with cron- o- meter when I get the chance. I've been meaning to figure one out anyways.
thank you!
This still comes up shy on calcium and Vitamin D, about half in both cases. Ghee might raise both of those, if you can have it. Lard, in a large enough quatity will take care of your Vitamin D.
1 T- Bone steak
1 c asparagus
3 cups (cooked) spinach
4 large eggs
6 0z wild Atlantic salmon
1 oz almonds
1 small bell pepper
1 jalapeno
1 mug white tea
1 avocado (hass, small)
5 cherry tomatos
1 cup chopped carrots
1 cup broccoli
Everything cooked in lard or grilled except the cherry tomatos, avocado, and salmon (salmon is assumed to be smoked.)
Why go through all the trouble? Simply use supplements and try to eat healthy, wholesome food that's as organic and grain/corn/omega-6 free as possible. I think that it's nearly impossible for most people to get all the required nutrients without also using supplements. For example, where I live nobody's ever heard of something like "grass-fed". The most I can hope for is organically grown vegetables ... which unfortunately usually still means monoculture and nutrient-deprived, it simply means less contaminated with chemicals and/or antibiotics.
Find me at aToadontheRoad.com. Cheers!
Wow nobody mentioned liver yet?