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Бен 10: Инопланетная сверхсила 1 сезон 20 серия
Ben and Gwen are forced to go to Darkstar for help when they come across their most deadly, most unexpected foe yet.
Бен 10: Инопланетная сверхсила 1 сезон 19 серия
When Ben and Gwen are faced with an unexpected enemy, Gwen turns to Darkstar for help.
Бен 10: Инопланетная сверхсила 1 сезон 18 серия
When Argit and the Vreedle Brothers team up, they come up with a plan to attack the Plumber Academy.
Бен 10: Инопланетная сверхсила 1 сезон 17 серия
With Kevin having absorbed Ben's aliens and become insane once again, Ben and Gwen have to go to the null void prison in an attempt to stop Kevin from destroying all the people there. We learn more about how Kevin got his material absorbing powers. In the end Ben thinks Kevin should be put down.
Бен 10: Инопланетная сверхсила 1 сезон 16 серия
Azmuth is upset with Ben, Gwen, and Kevin for letting Aggregor get all the pieces of the Map of Infinity. Ben resolves to become Alien X to get Bellicus and Serena to cooperate. When this fails, Paradox arrives and changes Alien X back to Ben. After a brief argument, Paradox and Azmuth explain that the Forge of Creation is where all
Бен 10: Инопланетная сверхсила 1 сезон 15 серия
With the fourth piece of the Map of Infinity almost in Aggregor's clutches, the gang chase him through the artificial planetoid known as Perplexahedron.
Бен 10: Инопланетная сверхсила 1 сезон 14 серия
Gwen, Ben, and Kevin follow Aggregor into a mystical dimension called Legerdomain with the help of Charmcaster, who was born there. They believe Aggregor's target to be the Alpha Rune worn around Addwaitya's neck, which they suspect is a disguised piece of the Map of Infinity. Gwen and Charmcaster work together several times, and the two develop a
Бен 10: Инопланетная сверхсила 1 сезон 13 серия
Ben, Gwen, and Kevin travel to the center of a water planet, namely Pisces, Ripjaws' home planet in an attempt to find Aggregor. They end up getting help from Magister Pyke, one of Ripjaws' species. The planet has a 2% solid core, which holds an instrument that holds the planet's water together in a gravitational pull. Aggregor takes the
Бен 10: Инопланетная сверхсила 1 сезон 12 серия
After "helping" Ben, Gwen, and Kevin stop the Forever Knights, Cash and JT start a TV show with Oliver as their cameraman. Ben, Gwen, and Kevin play along for awhile. Upon tracking the Forever Knight, Ben, Gwen, and Kevin learn what the Power Decoupler is being used by the Forever Knights and attacks them with some interference from Cash and JT.
Бен 10: Инопланетная сверхсила 1 сезон 11 серия
With his new powers, Aggregor easily defeats Ben and his friends. Azmuth tells Ben that Aggregor intends to steal the Map of Infinity, a complete map of the entire universe. With it, he'll be able to find the Forge of Creation, the birthplace of the universe. He further explains that Aggregor stole the five aliens' powers to protect him from the
Бен 10: Инопланетная сверхсила 1 сезон 10 серия
With all five aliens in his possession, Aggregor tries to return home to absorb their powers. A Plumber ambush damages his ship, so he instead heads for Los Soledad, in order to use Prof. Paradox's time machine as an alternate power source. Despite Ben, Kevin, Gwen, and Grandpa Max's best efforts, Aggregor succeeds in absorbing the aliens' forms
Бен 10: Инопланетная сверхсила 1 сезон 9 серия
Ben meets his favorite superhero idol Captain Nemesis when he shows up to save actress Jennifer Nocturne, however Captain Nemesis is displeased when Ben gets kissed by the actress instead of him. Captain Nemesis' assistant, Simons meets up with Will Harangue about staging an event that will put Captain Nemesis back on the front page of each
Бен 10: Инопланетная сверхсила 1 сезон 8 серия 'В
After ambushing Ben at his house, Ra'ad (one of the aliens captured by Aggregor) blames him for letting Aggregor recapture the other four aliens through the Ultimatrix. While trying to resist being scanned by the Ultimatrix with his electric powers, Ra'ad causes it to malfunction, and ends up deactivating it while both his and Ben consciousnesses
Бен 10: Инопланетная сверхсила 1 сезон 7 серия
When Ben, Kevin, and Gwen investigate an odd string of thefts by the Forever Knights, they learn that Argit is using Andreas (one of the aliens being hunted by Aggregor) to frighten them into making him their leader. Argit's plans go bad when the Forever Knights eventually revolt, and throw him into a dungeon with Ben and the others. When they
Бен 10: Инопланетная сверхсила 1 сезон 6 серия 'Не
Eager to escape his containment suit, P'andor (one of the aliens who were captured by Aggregor) offers a huge cash reward to anyone who can break him out of it. Everyone who tries to break the suit ranging from Buzz, Hammer, and Surgeon fails, save for Kevin, who uses a piece of Taednite to pierce it. But, when Kevin's Plumber's badge senses that
Бен 10: Инопланетная сверхсила 1 сезон 5 серия
In the Himalayas, Dr. Animo and his mind-controlled Yeti were fighting with Ben. Ben, as Brainstorm, mind-controls the Yeti and frees him. Kevin and Gwen deactivates the Devalution bomb. Gwen calls the Plumbers to take them, the Plumbers informed them that there is an alien action in Bellwood. Ben encounters the turtle-like alien Galapagus, who
Бен 10: Инопланетная сверхсила 1 сезон 4 серия
After defeating Ssserpent with Fourarms, Ben's celebrity status lands him in a starring role in a video game. The developers ask him to show off all his aliens' different moves so they can be implemented into the game. In reality, TV reporter Will Harangue has been using the video game to build a giant spider-like robot that knows all of Ben's
Бен 10: Инопланетная сверхсила 1 сезон 3 серия
After Jimmy Jones reveals Ben's identity, some of his enemies (such as Zombozo, Rojo, Charmcaster, Vulkanus, and Sevenseven) team up to try to attack his family. Now Ben and his friends have to protect them no matter what.
Бен 10: Инопланетная сверхсила 1 сезон 2 серия
Ben needs to be at more than one place at once; he has to fight the Forever Knights with Kevin, go to Julie's tennis game, and see a Sumo Slammers movie. So Ben transforms into Echo Echo and divides in three and changes back to Ben, but this time there are three Bens with one part of his personality. One is the real Ben, the other is the honest,
Бен 10: Инопланетная сверхсила 1 сезон 1 серия
Ben's secret identity is revealed to the entire world, and he is now a megastar superhero loved and favored all over by kids, teenagers,and few adults, but distrusted by many adults. After having a long talk with Julie in Ben's brand new car, Kevin and Gwen arrive in Kevin's car and tell Ben where to find the one who told his secret to the world
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WHEN Dr. Louis Aledort, and his wife, Ruth, above, bought their six-room apartment on the Upper West Side in 2001, they interviewed several architects, and mentioned to each of them that they had an art collection. Only one architect, Richard Lavenstein, a partner at Bond Street Architecture & Design in Manhattan, asked to see the collection.
"No one else said, what kind of art? What period is the art?" Dr. Aledort said.
Mr. Lavenstein was hired.
The Aledorts, who own mostly lithographs, by Robert Rauschenberg, Elizabeth Murray and Jasper Johns, not only wanted to light their art well, they needed to compensate for the fact that part of the apartment faced a courtyard and had very little light.
They could have lowered the 10-foot ceilings to put in recessed lighting, but Dr. Aledort, a hematologist, and Mrs. Aledort, who works with breast cancer patients in a support group held at Mount Sinai Medical Center, didn't like that solution. Instead, their architect collaborated with Francesca Bettridge, the president of Cline Bettridge Bernstein Lighting Design in Manhattan, which handled lighting for most of the public spaces of the Time Warner Center.
Ms. Bettridge designed a lighting scheme that is "relaxed," she said, and that "makes people comfortable." She washed the walls gently with light, using tiny but elegant fixtures to create museum-quality lighting. Because most of the couple's art is under glass, she took care to minimize reflections.
In every room, she chose each light fixture to fulfill a specific function. There is not a random fixture in sight, and all of them are on dimmers.Continue reading the main story
In the foyer, which has black and white prints, "I wanted to add layers of light, and balance the room," she said. A translucent torchier glows in the far left corner, spreading its diffused light on the ceiling and on the wall, while also lighting the art to its left. On the right side of the foyer, translucent sconces flank the entrance to the living room, creating more light on the ceiling and walls, while also shedding light on two pieces of art. The light is soft and warm. The foyer is clearly an entry, a hallway with art, but it is not an art gallery.
In the living room, top, she and the architect used low-voltage halogen track lighting covered with aluminum bands that were painted to match the wall. The light fixtures, called Valux, from Nulux, hold MR16 lamps, each with a reflector, so light from the bulb bounces off the reflector and shines down on the art.
Because the lamp also has a ribbed lens the light is spread more evenly. (The track was $140 a square foot and the lamps were $215 each.) "When you're lighting a piece of art, you aim the light at 30 degrees, and if it's a shiny surface you come at the art at an even steeper angle," said Ms. Bettridge, who aimed the light in the living room at 15 degrees. "Here we are washing the wall to get a wide spread of even, soft light."
The hallway, left, was too narrow for that approach, so Ms. Bettridge inserted track lighting in the center of the ceiling and pointed the lighting in two directions. Mr. Lavenstein designed the dining room to open onto the same narrow hallway, and frame a lithograph by Susan Rothenberg in the hallway, extending the room and making the art more important.
The clients were willing to drop the dining room ceiling six inches to conceal structural beams, which allowed the use of recessed lighting. Ms. Bettridge used Edison Price quartz wall washers. Each lamp has a small, tightly wound filament and a reflector. The reflector provides an even wash from the top to the bottom of the wall. (Washers are $260 each.) For the kitchen ceiling Ms. Bettridge used Leucos recessed lights with a translucent glass trim, and inserted MR16 low-voltage incandescent lamps. The light illuminates the glass trim and puts light onto the ceiling and floor. Under the cabinets for task lighting she placed incandescent tubular lamps.
It's only in the kitchen that the Aledorts have no art. After all, food splatters.Continue reading the main story

subset: commoncrawl


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The Tiger is back. The golf superstar is now ready to be back on his track. Golf has been his world and he has been famous because of this. He displayed incredible skills in this field by being a champion in a lot of tournaments. He gained a lot of sponsors and endorsements of various expensive products.
Late last year, he faced a scandalous event which has been creating a tremendous damage not only to his personal life but also to his professional life. An issue that a married man like Tiger can't avoid. The scandal has been so hot and because of his fame, the whole world has been sharing it from then on.
After quite sometime, though the fuzz is still there, Tiger is somehow ready to be back on track again. He would be back on his professional world soon. They are expecting him on the Masters which would take place this coming April 8. Those scandals are moral issues. It is not a hand injury that could affect the way he sways his golf club. Tiger Woods of Golf is Tiger Woods and no one could replace it.To receive updates from WWNN.co.uk directly to your e-mail, you can subscribe to us for free by entering your e-mail address here and confirming your subscription by clicking the link that will be sent to your e-mail address.
Topics: Golf, Tiger Woods


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Introducing the Kora and Cali dresses
Classic designs with a twist. Beautiful soft organic cotton. Practical and comfortable for pregnancy and beyond.
Organic. Sustainable. Made in the UK
good for you. good for the environment
We've ensured every step of our production process is as ethical and sustainable as possible.


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Around 90% of what is perceived as taste is actually smell, so why let alcohol burn disrupt true appreciation of our whiskies and spirits? Alcohol burn always gets in the way, masking not only pleasant aromas but some of the most important.
Using Naturally Engineered Aroma Technology (NEAT), the neck of our glass intensifies the aromas of your whisky, gin, rum, cognac or any other spirits whilst the flares spread the alcohol molecules, separating essences from ethanol.
Giving you, what we call, the 'sweet spot' where you can nose without the astringency of alcohol burn.
Not only that, the flares also provide a more thorough tasting experience by pouring the liquid all over your palate – as opposed to only in the centre of your taste buds like other conventional glassware.
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We've developed this course based on real client experiences.
Each day will focus on a theme with specific content! Inside the course, you'll find:
You will receive a full 30 days of nutrition support for $75.
When you sign up we'll send you reminders to get you excited about starting the challenge!
Interested in learning more?
Sign up for a free sample day!
Get Your Free Sample Day
Here's the thing. A single "Perfect Diet" doesn't exist. At Vitality Nutrition we don't believe there's a single, positively, without-a-doubt, best diet for every person to follow for their entire life. If we thought it would be effective to prescribe cookie-cutter diets we absolutely would. Our jobs would be a heck of a lot easier!
The good news? You can still lose weight, improve your brain power, increase your energy, and perform your best. You can do this by building you knowledge and awareness while aligning your habits with your goals.
The Take 30 Challenge focuses on our most successful strategies for forming new habits while building your knowledge of nutrition. It's not about dieting; it's about developing a healthier relationship with your food and your body.
How Take 30 Works
Ready to take the challenge? Create an account and pay a one time fee. Each day you will log into your account to receive the daily challenge.
We've developed a unique, engaging program full of expert advice, recipes, nutrition information, and inspiring stories from people just like you!
Ready to get started?
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Real Client. Real Results.
The Take 30 Challenge is designed with day by day instructions on how to live, eat, and feel better.
Our bodies are beautiful regardless of their size or shape. But we can't forget the real reason why it's so beautiful. It's beautiful for allowing us to be here today and for all things it allows us to do in this life, so we have to learn to take care of it.
Taking time to take care of myself inside and out has completely changed my outlook on myself. Thirty pounds lighter, stronger mentally and physically and more secure with myself than ever before, I now have the tools to keep my health a priority. This journey has been a marathon and not a sprint and because of that I know more about myself than ever.
When people ask about my weight loss journey I tell them my key to success is from daily consistency with my macronutrient and fibre intake alongside fundamental habits like exercising, going for a daily walk, drinking 6-8 glasses of water, getting a good sleep, and keeping stress minimal. Do I do this every single day? I do not. Do I do this 80% of the time? You bet.
Habits don't drain willpower!
Habits don't drain willpower. Willpower is like a muscle that gets tired with overuse! Habits are the actions we follow through on without even thinking about it. Learn the 30 core habits that have helped Vitality Nutrition clients acheive their goals.
Macro Tracking 101
Habits are the base but you'll need to build some baseline knowledge and awareness, too. Aside from habits, the course will focus on understanding the three macronutrients (protein, carbohydrates, and fats) and how obtaining the right mix of macronutrients will allow you to achieve nutrition success. You can't change your body without first investing time into building your knowledge and awareness!.
Not Sure if you're ready for a course?
No problem!
"We are what we repeadedly do. Excellence, therefore, is not an act but a habit."
- Aristotle


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Aug 29, 2018
COLUMBUS, Ohio – Ohio Dominican's men's golf team tees it up on Tuesday, September 4 as they host the Ohio Dominican Kickoff Classic at Jefferson Country Club in Blacklick, Ohio.
The men's golf team bring back seniors Drew Wayman (Wapakoneta, Ohio) and Jack Casebolt (Pickerington, Ohio) who each competed in 11 events for the Panthers a year ago.
Fellow returners include junior Blake Saffell (Somerset, Ohio) along with sophomores Jonathan Dill (Sedalia, Ohio) and Alex Heban (Toledo, Ohio) who all played at least six events a year ago. The Panthers also bring in five freshmen to the squad.
After the ODU Kickoff Class, the men's golf team has a quick turnaround as they head to Red Hawk Run in Findlay, Ohio on Saturday, September 8.


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12 Hilarious, Wise And Inspiring Quotes From Gabourey Sidibe's Memoir
This Is Just My Face: Try Not to Stare is Gabourey Sidibe's new memoir, just in time for summer reads! This memoir isn't your ordinary celebrity memoir as it's well written and touches upon some of Sidibe's deepest childhood scars, struggling with depression and other personal stories. Although Sidibe's experiences can be intensely overwhelming, her voice is observant and humorous. Here are some quotes of wit and wisdom she offers to readers:
1. "I could lose weight. That is a fact. But I am dope at any and every size. I am smart. I am funny. I am talented. I am gorgeous. I am black. I am fat. Sometimes I'm a bitch. At all times, I am a bad bitch. (The word bitch is pretty confusing, right?)"
2. "I was born a cynical, suspicious, forty-five-year-old divorcée."
3. "My hair and I have been through a lot (it's been on fire twice). There are many more hair battles to come, but I know my strength and beauty start at the roots. I've realized that black women have the most beautiful hair: long hair, weaves or natural; bobs, cut straight or asymmetrical; braids; dreads; Afros; shaved bald; faded with a flat top. Our hair can be anything! Choose a color, choose a texture, and our hair can do it. There's an entire Black Woman Hair Universe of Possibilities."
4. "I hate that sarcasm is hard to convey in a text. There should be a special font for sarcasm so people can tell when I'm being an asshole."
5. "Fuck Twitter yo. I hate Twitter. I love Twitter. I need it to get through a day, but it is also systematically messing with my health and sanity."
6. "Honestly, I block people [on Twitter] all the time … I prefer my world to smell like strawberries and look like rainbows, and at least on Twitter I can block negative comments, fighting, and opinions about what I should be doing in my life. I am compulsive about keeping those dark clouds out of my life. Choose your tweets wisely."
7. "I sometimes get so mad at myself. Mad at my body. I call it 'my personal 9/11' when I am feeling really down. My body sometimes feels like a tragedy. But I'm trying very hard to change my mind about that. This is my body. It's going to be with me forever. For all the ways it's failed me, it's come through for me a million times more."
8. "I'm still pretty boy crazy. My mom tells me to call them 'men,' but that seems too grown-up a word, and frankly, I'm not there yet. But to clarify, when I say 'boys,' I definitely mean age twenty-five and older."
9. "I usually spend my Friday nights out with my friends. Or I spend them at home writing. Most important, I spend my Friday nights doing whatever I want to do. I know that if I had a boyfriend, or, even worse, a husband, I'd spend my Friday nights compromising. I don't think I really want to do that yet."
11. "How many psychics does it take to convince a sad little girl that she can be much more than the world is telling her she is? None. She's got to be able to convince herself to show up for her own life."
12. "When I say that I'm beautiful, I don't say it so someone will clap and think I'm brave. I'm not doing it so that someone will comment on how confident I am. I don't say it with ego and I don't say it defensively. I don't say it meaning that people who look like me are better than people who look like you. I say it because I believe it. I've earned every centimeter of my beauty."


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Method for inferring behavioral characteristics based on a large volume of data
A method provides for mining information from large volumes of data regarding transactions. The method provides for inferring a behavioral characteristic of a party to the transaction based on a large volume of data concerning a multitude of parties. That inferred characteristic may be dynamic in nature.
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This application is related to and claims priority from provisional application No. 60/079,320 entitled TELECOMMUNICATIONS DATA MINING, filed Mar. 25, 1998, the disclosure of which is hereby incorporated by reference.BACKGROUND
The present invention relates to extracting information from large data sets. More specifically, the present invention relates to a method for inferring behavioral characteristics of an individual entity based on a large volume of data about a multitude of entities.
Data mining is the name given to the process of extracting information from large data sets. It is a multi-disciplinary field that brings together the latest technology and research in database design, exploratory data analysis, algorithms for very large data sets, model fitting, visualization and systems.
Many will claim that data mining is nothing new. Database systems have been around for decades. Also, and for centuries, scientists have studied data sets for correlations and dependencies between the elements. They have developed models to describe data and invented ways of displaying both the data and the models.
However, data mining differs from "traditional" data analysis on one single, but important dimension: scale. The size of data sets has grown tremendously with modern computers and data storage capabilities. Ten years ago a large data set would consist of thousands of observations/cases stored in 10 MB. Today, a large data set consists of millions of cases stored in 10 GB. The size and complexity of the available data is often so overwhelming that most algorithms from machine learning, statistics, and visualization become impossible to use in practice. An important problem in data mining is to determine how to scale these algorithms. This problem is referred to in "Statistical Inference and Data Mining", Glymour et al., Communications of the ACM, Vol. 39, No. 11 (Nov. 1996) 35-41 and "External Memory Graph Algorithms", Chiang et al., ACM-SIAM Symposium on Discrete Algorithms, 1996.
Large data sets are ubiquitous in science, technology, and the commercial sector as described in "Mining Business Databases", Brachman et al., Communications of the ACM, Vol. 39, No. 11 (November 1996) 42-48; "Citibank Mines Data", Information Week, No. 600 (October 1996), and "A Collaborative Filter Can Help You Mine Data For Jewels", InfoWorld, Vol. 18, No. 49 (December 1996), 47. For ease of reference and explanation, the commercial sector is addressed since this is where the inventors' recent experience lies. Here, the data sets may be the result of credit card transactions, retail sales, or calls carried by a phone company. In a highly competitive marketplace these data sets are an extremely valuable asset. From these data, businesses can learn who their customers are, where they are, what their needs are, how they use existing services and products, what makes customers stop using or buying the offered services and product, and what offers could attract new customers. Businesses that do not have a method for efficient recording, handling, and analyzing data are at a serious competitive disadvantage. Thus, data mining is more than a research area, it is a commercial need.
Unfortunately, often times since the number of commercial transactions is fairly voluminous, the amount of data recorded per transaction is somewhat limited. Thus accounting—like information might be retained for each transaction. But, it would be beneficial if further information about a transaction or the participants to the transaction could be gleaned from this data set.SUMMARY OF THE INVENTION
The present invention provides techniques and methods for performing meaningful mining of gigabytes of data. In accordance with the present invention, a volume of data relating to a multitude of transactions is maintained. The volume of data is analyzed to determine inferences about characteristics of the entities or parties conducting the transactions.
Making inferences from data has been a long tradition in the field of statistics. The present invention provides the ability to make inferences from data when the size of the data set precludes conventional analyses. Data streams may comprise transaction records (transactions) that capture the salient details of a transaction between two entities. Examples of types of transactions to which the present invention can be applied include such things as credit card purchases; telephone call records, packet headers in data communications; and stock transactions.
The present invention further provides the ability to make inferences pertaining to dynamic behaviors.
In one embodiment of the invention provided to illustrated the principles of the present invention the transactions are calls along a communication network. Billing records for the calls are maintained. Characteristics about individual calling or called parties can be inferred from the billing records. Examples of such characteristics include the likelihood that the party is a business or a resident, the likelihood that the communication is a facsimile transmission, the likelihood that the communication is somehow fraudulent in nature or other similar types of characteristics about calls, or calling and/or called parties.BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 illustrates, in tabular form, an example of a transaction record which could be used in an embodiment of the present invention.
FIG. 2 illustrates historical information used in connection with an embodiment of the present invention.
FIG. 3 illustrates coefficients for use in a model in an embodiment of the present invention.
FIG. 4 illustrates, in block diagram form a representation of an apparatus for performing a method in accordance with an embodiment of the present invention.DETAILED DESCRIPTION Overview
The present invention provides a method by which data is mined or extracted from large data volumes or sets. In the present invention it is recognized that a large volume of transaction data can contain information about the parties engaging in the transactions or the nature of the transactions themselves. The present invention examines the volume of data to ascertain whether correlations exist between particular pieces of the data and particular types of transactions or transaction parties. Once a model or set of rules is established based on this analysis, the model or set of rules can be applied to the individual parties to examine their behavior in relationship to these rules and to classify these parties accordingly. This process can be referred to as making inferences about the parties or the underlying transactions.
Making inferences from data has a long tradition in the field of statistics. The unique perspective in the present invention concerns an instance of making inferences from data when the size of the data set precludes conventional analyses. In particular, the number of records can essentially be thought of as being infinite, since they occur continuously through time. The discussion is limited to data streams that consist of transaction records (or simply transactions) often on the order of 10 million per hour. A transaction can be constituted by a very simple record capturing the salient details of a transaction between two entities. Examples (but not the only possible types) of transactions include:
a) credit card purchases (in retail industries);
b) telephone call records (in voice communications);
c) packet headers (in data communications); and
d) stock transactions (in banking and finance).
Typically the salient details consist of:
1) the originator of the transaction;
2) the recipient of the transaction;
3) the date of the transaction;
4) the time (of day) of the transaction; and
5) the "size/value" of the transaction.
The present invention provides for making inferences about all originators and recipients that conduct transactions. As relates to a)-d) above this translates to:
a′) all card numbers and all merchant IDs;
b′) all telephone numbers;
c′) all IP (internet protocol) addresses; and
d′) all investor IDs and publicly traded corporations.
Inferences about all entities are conventionally accomplished by accumulating all the data and applying statistical or other techniques. In the case of transactional data streams this is not possible owing to the size of the data and the fact that the data set continually changes through time. In this regard a distinction can be made as to what size "learning increment" is used. The present invention applies to "learning increments" that include:
i) processing single transactions as they arrive (termed event-driven processing); or
ii) accumulating transactions over some time period and processing them in a batch (termed time-driven processing).
It is worth noting that the conventional methods pertain to processing model ii) where the "batch" includes all of the transactions and they are processed in toto.
Most often, a particular application for the inferences dictates the relevant "learning increment". For example, fraud detection or network intrusion detection requires event-driven processing since immediate detection is necessary. By contrast, marketing and service management applications suggest daily/weekly time-driven processing cycles.
The accuracy and rate of inference is affected by the processing time cycle: behaviors that are easy to infer over a first time period might be extremely difficult to infer over a second, shorter, time period.
Clearly, processing time is also an important constraint in choosing the appropriate cycle time for incremental inferences since the time it takes to process a group of transactions has to be less than the cycle time (for example, it must take less than 24 hours to process a day's worth of transactions). The types of inferences that can be made from transactional data streams depend critically on the extent to which statistical patterns of certain characteristics are exhibited in the data stream. The present invention applies to cases where such statistical patterns exist. But the patterns need not be direct measures of the attribute that is being inferred. That is, individual transactions provide only indirect evidence of some behavior that is of interest. For example, one can infer "wealth" by observing credit card transactions even though individual records contain no information on the wealth of the card number because collections of such records over time and the statistical property or properties of card numbers associated with wealthy individuals allows reasonable inferences to be made over time.
Finally, the present invention pertains to behaviors that are dynamic—that is, that change through time. In the previous example, the "wealth" of a particular card number is not expected to be constant (for example, as earning power increases, spending does as well). It is usually of interest that inferences be current, so that they reflect behavior today, not from the more distant past.
The remainder of this application will describe the application of the principles of the present invention to one of the example transactions referred to above, the area of telephone call/communication transactions. The techniques for mining data in the telephone call transaction area can be readily adapted to these other types of transactions.Transaction Characteristics
Depending on the types of transactions being monitored, different characteristics about the transactions and/or the transaction parties may be of interest.
Looking at telephone calls as an example of transactions, a single service provider may participate in a multitude of transactions.
For example, AT&T is probably the largest phone company in the world. It carries about 250 million (M) calls on a weekday—less over the weekend. The total number of calls reported for 1997 was 72 billion. On a typical weekday 65 M unique phone numbers are observed by AT&T. These numbers include those of calling parties as well as called parties. Over a 40 day period 300 M unique numbers may be observed.
Call records are generated with respect to each of these transactions or calls. An example of call detail records is illustrated in FIG. 1. In table form are shown records for each processed call, showing the telephone number (TN) of the originating party (100), the dialed telephone number (120), the time the connection was made (130), and the duration of the connection (140). It would be beneficial if further information about a given party, as represented by a telephone number, could be derived based on data relating to that party as compared to the universe of data relating to all calls and call parties.
A universe list is a list of unique telephone numbers (TNs) observed on the network, together with a few bytes of information on each to support operations such as billing, customer care, usage management, and fraud detection and prevention. In accordance with an embodiment of the present invention, at least four distinct variables can be monitored through time.
inactivity: the number of days since the number was last observed on the network.
minutes: the average daily number of minutes a TN is observed on the network,
frequency: the estimated number of days between observing a TN,
bizocity: the degree to which a TN displays business-like behavior.
These variables may be maintained separately for inbound calling and outbound calling, and in some cases, distinctions can be made for toll and toll-free calls.
The inactivity variable may be determined by simply incrementing values in a data structure according to the expression: INACTIVITY  ( TN ) new = { 0 if   observed   today INACTIVITY  ( TN ) old + 1 if   not   observed   today ( 1 )
That is, if the telephone number (TN) is observed today then the inactivity variable is re-initialized; if the TN is not observed then the prior inactivity variable is incremented by one.
The basic statistical device used for tracking the other three variables is an exponential weighted average. This strategy is typical in tracking large numbers of entities with even larger numbers of transactions. For example, letting 0<&lgr;<1, the expression:
MINUTES(TN)new=&lgr;MINUTES(TN)today+(1−&lgr;)MINUTES(TN)old (2)
illustrates how today's minutes for a TN are combined with the old average daily minutes to provide a smooth current estimate of average daily minutes. Similar computations are made for "frequency" and "bizocity".
This is the main principle behind the universe list, namely using old and current information about a party, as well as information from the entire data set, to better describe characteristics of that party.
The universe list is based both on appropriate statistical modeling, and an efficient computational approach. Subsequent sections, present a statistical method for use; the need and the method for monitoring a bizocity score (bizocity is an interesting behavioral factor that requires challenging statistical modeling and tracking methods); a computational engine necessary to maintain a call detail stream and to update data structures (a careful design of the data structures is of critical importance: when processing hundreds of millions of calls per day, the I/O processes might tend to dominate the CPU time, and thus considerably slow down the whole processing); a description of an interface to data mining results. It helps to give a convenient access to the data, and to make testing possible when the size of the data exceeds several gigabytes.Bizocity and Its Modeling
One natural piece of information that AT&T is interested in is whether a phone number belongs to a business (Biz) or a residential (Res) customer. Historically, the company has split its operations along the Biz/Res boundary for billing, customer care and marketing. So, it is naturally desirable to infer Biz/Res status of any TN observed on the network to aid internal operations. For example, when a call comes into an AT&T customer care center, knowledge of the Biz/Res status of the calling party allows the call to be automatically routed to the appropriate care channel. This is an efficient use of the channel and a convenience to the calling party. Similarly, fast, efficient investigation of telephone fraud requires knowledge of the Biz/Res status of the involved lines. For example, long calls originate quite commonly from residences late at night, but not from a business. Thus, a long call from a business at night may be an indicator of an instance of fraud.
For about 100 M of the 300 M lines seen on the network, the self-reported Biz/Res status is known: when a customer signs up for phone service with the local phone company, they specify their status as either business or residence. If the customer chooses AT&T as their long distance service provider, this information is usually passed on to AT&T. Feeds of information from the local phone companies may be incomplete (e.g., a business may have its central number listed as a business, but may not also identify all of their additional lines). The information can also be out of date (e.g., the feeds may arrive much later than the date the customer starts using AT&T long distance service). Additionally, a customer may change his or her usage of a line such as for example changing a residential line to a line used for home business purposes. Such information is of course not normally conveyed to AT&T.
The universe list solves the problem of the missing Biz/Res status for the remaining 200 M lines (or ⅔ of all lines seen) by deriving and maintaining a behavioral estimate of the Biz/Res status of all TNs observed on the network over some predetermined period of time (e.g., the most recent 40 days). This behavioral estimate is referred to as the bizocity score of a telephone number. Bizocity then is a probablistic measure of how much a line behaves like a business. It is constructed from characteristics of the calls going into or out of each line. The bizocity of a line may very well be different from the self-reported Biz/Res status. This will be the case for example when a residential line is used for a home business. Thus, while it is not possible to derive what a customer might have actually told their local phone company when signing up for service, based on the behavior of the line over time it is possible to derive a very good guess of the line's status.
The methodology of the present invention estimates the bizocity of a phone number by tracking the behavior of the line over time. Each day all lines with calling activity can be scored for how business-like they behave that day. This score is combined with the bizocity score from past history of the line to form a new bizocity score. These new scores are stored for next day's processing. Alternatively, one could use a larger time interval for determining bizocity. For example, rather than updating such scores on a daily basis it could be done on a weekly basis or on some other time period. This would likely increase the data to be used for score adjustment and may provide a smoother measure of the characteristic.
As indicated above, a basic building block for the universe list is a call detail record generated at call completion. Most of the fields in the record relate to how the call is routed on the network. Only a few fields contain useful information for bizocity modeling. A small sample of call detail records is shown in the table of FIG. 1. It is apparent that these records do not provide customer identification, only pairs of phone numbers: they have the originating TN, the dialed TN, the connect time, and the duration of the call. For credit card and collect calling, other fields can identify the billing number. While such calls may have some impact for bizocity scoring, it is believed that effective information can be derived without referenced to such credit card and collect calling calls.
It is possible to add value to the plain call detail records by dipping into other available data bases. As described above, for about 100 M TNs there is information available about the self-reported Biz/Res status so it is possible to add two fields to each call detail record, one that identifies the Biz/Res status of the originating and another that identifies the Biz/Res status of the dialed TN. This database also enables us to construct a training set to learn calling behavior. Using the training data we illustrate the (statistical) signal on bizocity in call detail.
FIG. 2 illustrates how businesses and residential customers make different use of the telephone. Businesses call during the week, but hardly ever over the weekend. Businesses call from 9AM-5 PM with a lunch break, while the peak for residential customers' calling is in the evening. Businesses make short calls, and they call other businesses or 800 services. FIG. 2 shows that while individual call detail records may be very simple and very noisy, this simplicity and noise can be overcome by sheer volume: from aggregation it is possible to obtain lots of information for differentiating businesses from residences.
The statistical patterns in FIG. 2 are aggregated over all known Biz/Res TNs for a typical week of calling. If one studies a single call from a TN one gets a much weaker piece of information. For instance, it is not possible to derive much information for a TN based on a single three minute call on a weekday afternoon. But by staging processing and aggregating calls, bizocity scoring of individual TNs can be significantly enhanced.
Processing can be staged over a 24 hour period. This serves the purposes of collecting more data (and hence getting a better signal from a single TN), and reducing the I/O burden on call processing. Recall that on a typical day the AT&T network carries 250 M calls but that only 65 M unique TNS are involved. Therefore, processing call detail every 24 hours can reduce the I/O by 75%. The flip side of staging the processing is that it is necessary to store 24 hours of data, and then sort it so that all the calls from a single TN can be processed together. Storage is a minor problem: disks are cheap and records can be compressed in an efficient manner. Specifically, it is possible to translate individual ASCII records to 32 byte binary records so that a day of call detail occupies a total of about 8 GB. Sorting is done directly from these binary records. It takes about 30 minutes on an SGI Challenge with 3GB RAM using a single processor.
Once all the calls from a single TN are gathered, its calling profile is formed by binning its calls into a four dimensional array, e.g., a data cube. The dimensions are day-of-week, time-of-day, duration, and Biz/Res/800 status of the detailed TN. The size of the entire cube is 7×3×4×3×65 M.
From training data collected on known businesses and residences it is possible to form logistic regression models for scoring a TN based on its calling profile. Logistic regression seems to do a reasonable job at this two group classification, but due to the near-rank deficiency of the binned data, regularization via a "ridge" penalty is necessitated. An advantage of logistic regression is that it leads to easily interpretable models. The coefficients of one of these models are shown in FIG. 3. It shows how long calls in the evening contribute towards scoring a TN as a residence while short calls in the middle of the day favor a business-like behavior.
The models are then used on the calling profile for the single TN to estimate today's business-like behavior of that TN. This is done for all TNs engaged in calling activity in that update period (here one day). Using just today's calls as a bizocity score would give a noisy picture of a TN. Therefore, it is more appropriate to track the TNs and smoothly aggregate their daily scores. The bizocity score of a TN, BIZ(TN), can then be updated using an exponential weighting as previously mentioned:
BIZ(TN)new=&lgr;BIZ(TN)today+(1−&lgr;)BIZ(TN)old (3)
where 0<&lgr;<1. The "aging" factor &lgr; is adjusted as a trade-off between smooth scoring of a TN (small &lgr;) and fast tracking in changes in behavioral pattern of a TN (large &lgr;). With a reasonably small &lgr;, the new bizocity score can contain weeks of historic information about TNs without storing more than one set of scores.
If a TN does not have any calling activity, its bizocity is also updated using Equation (3). From training data of known business and residential customers, its probabilities of how likely a TN is to be a business given that it makes no calls on a weekday or over a weekend have been estimated.
One interesting and important aspect of the universe list is that the bizocity scores are self-consistent and self-maintaining. Recall that in off-line training data, the calling profile of a TN is formed by binning new calls with respect to the Biz/Res/800 status of the dialed TN. But for on-line production scoring, the self-reported status for ⅔ of the TNs is not available. This deficiency is overcome by utilizing the bizocity score for the dialed TNs, BIZ(DIAL−TN)old, even when the self-reported status of the dialed TN is known. So, for example, a call will be dialed to a TN that is a Biz with probability 0.7 and a Res with probability 0.3. The calling profile of a TN is the array of all these probabilities, and the new bizocity score of the TN, BIZ(TN)today is computed from the old bizocity scores:
BIZ(TN)today=f(profile(TN, BIZ(DIAL−TN)old))
were f is the logistic regression model estimated off-line. The universe list has been running in this fully autonomous mode for more than a year and we still observe fine separability of Biz and Res lines: is still observed the bizocity scores have not collapsed into a single value.
The accuracy of bizocity scoring is hard to assess since a TN may very well behave differently from what it officially is registered as. This will be the case for example when a pure residential line migrates to home business use. Accuracy for predicting the official labeling of a TN has been shown to be 75%.
Having described the process of modeling and detecting a given characteristic such as bizocity, it may be beneficial to consider a generalized description of the process.
First, the process obtains a sample of labeled entities relating to the behavior or characteristic under study. Normally this sample would not be drawn from the transaction stream itself but rather from some auxiliary corporate database. The sample should consist of N ordered pairs
(entity-id, entity-value).
The entity-id's are used in the second step. The entity-value is normally binary indicating whether or not the entity exhibits the behavior or not. In other cases the value can take on values on a nominal (e.g., red/green/blue), ordinal (e.g. low/medium/high), or a measurement (e.g., $130,000/yr) scale.
Second, the process extracts (from the data stream) a sequence of transactions (over time) corresponding to the entity-ids. The length of the sequence is dictated by the behavior under study and the available resource for analysis.
Third, the process identifies features in the data stream that characterize, in a statistical sense, the behavior under study. The selected features will usually determine, or be determined by, the processing cycle. For example, if the best feature to characterize wealth of a credit card holder is the ratio of weekend purchases to weekday purchases, then the processing cycle cannot be sub-weekly. One or more of the salient fields of the transaction records could make up the feature set. Call these features the entity-features.
Fourth, the process builds a model to predict the entity-value from the entity-features. Depending on the type of entity-value at hand, the prediction model could be based on linear regression, logistic regression, decision trees, neural nets, or similar linear/nonlinear modeling technique.Computational Issues
The universe list is a particular embodiment of the present invention that requires serious hardware and robust, efficient software. Call detail records are stored in 32 byte binary records. At no point in the processing is ASCII conversion performed. Highly efficient programs work directly on the binary records. For example, sorting 250 M records can be accomplished in about 30 minutes (recall that records need to be sorted two ways since TNs are being scored based both on calls into and out of them). A set of tools have been developed for operating on the binary records to support filtering and tabulation.
FIG. 4 illustrates in block diagram form, a data mining platform on which the present invention can be executed. The platform includes a data warehouse, 410 that stores the various transaction records. It further includes a record producer 420 that receives the raw data stream and creates the records to be stored in the warehouse. A processor 430 executes software from module 440 to perform the analysis of the records in the data warehouse in accordance with the processes described above.
The universe list embodiment may be implemented in C. In a couple of hours it makes two passes over the data, first to score each TN seen with outbound calling, then to score each TN seen with inbound calling. This processing is done on the platform which can include an SGI Challenge with 20 processors, 3 GB RAM, and 5 GB of swap space. As one would expect when discussing large volumes of data, a several terabyte disk farm is maintained.
The scores are stored in special purpose data structures that allow for fast sequential and fast random access. These data structures reflect the semantics of 10 digit telephone numbers into the exchange (first 6 digits) and line (last 4 digits). First, they exploit the fact that the space of exchanges is sparsely populated—only 150,000 of the possible 1 M exchanges are currently in use. Second, we exploit the fact that exchanges in use are often densely populated is exploited, so all 10,000 possible line numbers for an exchange are stored in a contiguous block whether or not the exchange is actually fully populated. Inbound/outbound scores for a TN are quantized into one byte, so the data structures can take up about 2 GB for each variable (score). This is much more than is minimally required since a lower bound for the space required is one byte for a TN and one byte for the score, suggesting a 600 MB data structure. This smaller data structure was initially considered, but migration to the other structure was provided for fast random access. The data structure also deals effortlessly with growth within an exchange, and quite simply with area code splits.
Besides the three inbound/outbound measurement variables that are maintained for each TN, maintain activity maps for all TNs are maintained. Also, the method keeps track of how many days calling activity has not been seen on a TN inbound, outbound, or to 800-numbers. The upper bound for the number of passive days is 40. This allows packing the three activity values for the single TN into two bytes. The size of the activity maps is about 4 GB, so it is necessary to use 64-bit programming and to run our programs on 64-bit machines.Interface
Implicitly, the above methodology for call transaction analysis involves estimating several hundred million parameters per day. How can one explore such a huge parameter space and ensure that the algorithm is doing the "right thing"? The approach of this particular embodiment of the present invention exploits the fact that TNs are aggregated at several levels of a natural hierarchy:
bank: the group of 100 TNs that share the first 8 digits of the TN,
exchange: the group of 10000 TNs that share the first 6 digits of the TN,
area code: the group of (<10 M) TNs that share the first 3 digits of the TN,
state: the group of TNS that reside in the same state (i.e., a group of area codes),
region: the group of TNS that reside in the same region of the country (i.e., a group of states).
Similarly, in other types of transactions it is possible to take advantage of any natural hierarchy with regard to entity identifiers.
The data available can be made available at all these levels through a variety of visualization tools. In effect, the tools are designed to be used in a top-down fashion whereby one starts at the highest level of the hierarchy. As more detail is required, the user is able to "drill-down" and check results at a finer level. Depending on the application, information at the TN level is provided. For example, corporate security applications require direct access to a random-access data warehouse of call records.
For interactive use, it is possible to employ a web browser such as Netscape Navigator or Microsoft's Internet Explorer as the user interface. This has proven quite effective in disseminating the results to a broad community of users and platforms over a corporate intranet. The user can, with a few clicks, display scores for a full exchange and effectively drill down to the single TN level. The universe list contains about 150,000 exchanges so all images have to be computed on-the-fly. Snappy performance and rapid response is guaranteed by the special purpose data structures and C-code that directly computes images in GIF format.Conclusion
A universe list in the telecommunications call processing environment proves that it is indeed possible to do meaningful mining of gigabytes of data. The universe list has now been running uninterruptedly for more than a year, every day updating 7 bytes of information on every TN with calling activity. Over this year, the universe list has demonstrated how it is possible to establish and effectively maintain short, yet powerful, behavioral profiles on millions of telephone numbers through tracking of simple features.
The bizocity score described in this paper relates to a classification task: statistical models are used to categorize telephone numbers as business or residential lines. Another interesting task is that of capturing deviations: new transactions are compared to the existing profile of the telephone number, and large deviations are flagged for further investigation. This is methodology which can be used to catch international calling fraud.
While our experience is with call detail data from the AT&T network, the methods for tracking simple profiles described in this paper can, of course, be applied to other very large transactional data set such as airline reservations, credit card usage, and web-access. More specifically such large data sets, comprising primitive transaction records individually representing limited pieces of information, can be used to create models about characteristics concerning the transaction participants. Then, data concerning any one of these transaction participants can be analyzed with reference to the models to infer one or more characteristics of the participant.
1. A method for inferring a behavioral characteristic of an entity from a large volume of multi-entity transaction data, comprising:
- extracting N ordered pairs from a telephone number database, the N ordered pairs having a telephone number and a business status value indicating whether the telephone number belongs to a business;
- storing transaction data including a plurality of call detail records, each of said records having an originating telephone number, a dialed telephone number, a connect time and a duration;
- extracting a first sequence of transactions corresponding to the N ordered pair telephone numbers from the transaction data;
- identifying a plurality of features indicative of the business status value within the first sequence of transactions;
- building a model to predict the business status value from the features;
- extracting a second sequence of transactions corresponding to a telephone number of an entity from the transaction data;
- predicting a business status value for the entity using the model and the second sequence of transactions; and
- inferring whether the entity is a business from the predicted business status value.
2. The method of claim 1 further comprising:
- analyzing a subsequent set of call detail records using the model;
- determining a revised probability that the entity is a business based on the analysis of said subsequent set of call detail records and an earlier determined probability.
3. The method of claim 2, wherein said building the model includes processing staging and call aggregation.
4. The method of claim 3, wherein said processing staging occurs over a 24 hour period.
5. The method of claim 1, further comprising:
- forming a calling profile for each originating telephone number by binning the call detail records associated with the originating telephone number into a four-dimensional data array.
6. The method of claim 5, wherein said data array includes a day-of-week dimension, a time-of-day dimension, a duration dimension, and a status dimension.
7. The method of claim 1, wherein said model is formed using logistic regression techniques.
8. The method of claim 7, wherein said model is regularized using a ridge penalty.
9. The method of claim 1, wherein said building the model occurs over an update period.
10. The method of claim 9, wherein said update period is one day.
11. The method of claim 2, wherein said determining a revised probability includes updating the earlier determined probability based on exponential weighting and an aging factor.
12. The method of claim 1, wherein said model is based on linear regression.
13. The method of claim 1, wherein said model is based on decision trees.
14. The method of claim 1, wherein said model is based on neural nets.
|August 22, 2000
|Lindsay et al.
|September 12, 2000
|Basch et al.
|January 9, 2001
|Ramkumar et al.
|February 6, 2001
|Brin et al.
|February 13, 2001
|May 29, 2001
- Statistical Inference and Data Mining, Glymour et al., Communications of the ACM, vol. 39, No. 11, (Nov. 1996(35-41.
Type: Grant
Filed: Mar 25, 1999
Date of Patent: Nov 12, 2002
Assignee: AT&T Corp. (New York, NY)
Inventors: Corinna Cortes (New York, NY), Daryl Pregibon (Summit, NJ)
Primary Examiner: Kim Vu
Assistant Examiner: Tam V Nguyen
Attorney, Agent or Law Firm: Kenyon & Kenyon
Application Number: 09/276,448
International Classification: G06F/1730;


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A little known location, Loganlea Reservoir has a lot to offer. Changes greatly with the seasons, looks fantastic with snow on the hills. There are a couple of piers which make fantastic foreground subjects. The long exposure technique is really suited to this location! Very popular with fishermen, they could potentially make interesting candid subjects.
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shrine(redirected from enshrined)
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Related to enshrined: preamble
shrine:see pilgrimpilgrim,
one who travels to a shrine or other sacred place out of religious motives. Pilgrimages are a feature of many religions and cultures. Examples in ancient Greece were the pilgrimages to Eleusis and Delphi. Pilgrimages are well established in India (e.g.
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A place, building, or structure made sacred by association with a historic event or holy personage; an altar, tomb, or chapel.
a large casket in the form of a sarcophagus or box, or sometimes an architectural structure, often decorated with various pictures, precious stones, and the like and used to hold the remains of saints.
Shrines were set up in churches, usually in an elevated place under a canopy. Some shrines have great aesthetic value, such as the shrine of St. Sebaldus in the Church of St. Sebaldus in Nuremberg (bronze, 1508–19; sculptors, P. Fischer and sons) and the shrine of St. Sergius of Radonezh in the Trinity Cathedral of the St. Sergius Trinity Monastery (silver, 16th century, with a silver canopy from the 18th century).
A receptacle to contain sacred relics; by extension, a building for that purpose.
1. a place of worship hallowed by association with a sacred person or object
2. a container for sacred relics
3. the tomb of a saint or other holy person
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If bright pastels and soft, romantic hues just aren't your wedding setting forte, we have a feeling this styled shoot full of moody modern Texas wedding inspiration from Katie Frost Weddings might be just the thing for you! From our new Spring/Summer 2019 issue, we're obsessing over all of the details that make this editorial feel current but not overly trendy. Set
With our countdown to the 10th anniversary cover underway, we've been anxious to share our 10 favorite wedding cake trends from the past decade! And today's the day! You'll find creative cake ideas featuring everything from marble icing to naked cakes below thanks to the the incredible talents of DFW's local wedding cake bakeries. Take a peek! Marble Icing Loft22
Can you believe it's time to start our countdown the the 10th anniversary issue's release and the the all-new SS19 cover?! Over the next 10 weekdays, we'll be sharing a few of our favorite details and trends in celebration of our 10th anniversary issue. Today, we're kicking off the countdown to January 30th's reveal with our 10 favorite wedding altars
Best of 2018 Wedding Decor Trends
With so many different style options to choose from in the planning stages of a wedding, it's always fun to see how planners and brides create waves in local decor trends, so we've taken a look back in our year end roundup of some of the best of 2018 wedding decor trends and doing the near-impossible task of selecting a
Best of 2018 Wedding Floral Trends
With so many stunning weddings to reflect on since the beginning of the year, we had to take the opportunity to sing praises to all of the unbelievable, often jaw-dropping floral arrangements we saw in real weddings and styled shoots in our year end roundup, taking a look at the best of 2018 wedding floral trends. Though all prominent to
Best of 2018 Bridal Fashion Trends
As we take a look back at all of the fabulous bridal looks that came across our desks, we can't help but to reflect on some of our favorite new styles of the past 12 months, so we bring you a year end roundup, featuring the best of 2018 bridal fashion trends! From new shapes to experimental colors and even
If you're the type to go for bold tones over delicate pastels, you'll absolutely love this wedding inspiration full of bright, blooming colors, dreamed up by Katie Frost Weddings! Statement-making florals by Billie Ball & Co. anchored this beautiful styled shoot hosted by Flying V Ranch, and the fuchsia tones in the bouquets made their way into the stationery by
We absolutely love seeing wedding inspiration from a completely unique perspective, and this wanderlust inspired styled shoot by Katie Frost Weddings was just that! Inspired by the colors and terrain of Big Sur in California, a love of travel and a love of dogs, of course, this bright and cheerful editorial gives us all the happy vibes and incorporates so
Katie Logsdon Weds Alexander Abraham | Light Pink Country Club Wedding from Katie Frost Weddings
Kaitlynd DeWolfe Weds Bryce Parham | Colorful Romantic Wedding at Hickory Street Annex
Bethany Benson Weds Jhonas Locke | Whimsical Wedding at The Rosemary Barn by Katie Frost Weddings
Kiersten Weds Chris Eldridge | Rustic Ruby Red Wedding by Katie Frost Weddings


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Ramping Up To A New Vision Of Leadership
The Y is a cause-driven organization, and one of the leading nonprofits in United States. In the East Bay, the YMCA has served the community for over 135 years, as a resource for healthy living, youth development and social responsibility.
Why We Worked Together
In 2015, YMCA of the East Bay wanted to step towards a new vision of shared leadership throughout their organization, and break traditional hierarchies. The goal: to empower their employees with the agency to create change, no matter what role they held.
Their CEO partnered with LATE NITE ART to create a customized workshop that would allow his leaders to explore and co-design a shared leadership model.
What We Created
The LATE NITE ART experience served as a ramp up to a long term change initiative for YMCA of the East Bay. It set the context for why this change was taking place.
On Mar 18th 2015, we brought together a diverse group of the 'Top 50' of their directors & management, from 9 branches and 3 residential camps. The focus of our workshop was to:
Learn about each other beyond their roles, and collaborate.
Understand that they each have a voice that matters.
Co-design a new leadership model and culture.
How It Succeeded
Given that our biggest challenge was to break barriers within the team, and help them co-create a new vision, our imagination-rich space set the tone right. We:
Built a stronger sense of empathy and understanding between people.
Triggered clarity about how people could contribute to the new culture.
Opened new communication channels for all opinions to be heard.
Helped form a YMCA-centric definition of shared leadership.
Broke silos of departments, 'old timers', 'new folks' and management.


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Voting Rights
We're working to eliminate disenfranchisement and discriminatory voting practices in the Deep South.
Some Southern states retain Jim Crow-era laws that strip voting rights from people convicted of certain crimes, even after they've paid their debt to society. In addition, numerous states have adopted laws in recent years to suppress voting by vulnerable populations such as minority groups, the poor and the elderly.
Through litigation, legislative advocacy and education, the SPLC is working to end systematic voter suppression and to ensure that everyone has an opportunity to make their voice heard at the ballot box.
Through the Alabama Voting Rights Project, we're going door to door helping Alabamians restore their right to vote. After the passage of the Definition of Moral Turpitude Act in 2017, Alabama finally clarified which felonies will not disqualify citizens from voting. Now, tens of thousands of people are eligible to restore their right to vote through a simple application to the state.
Unfortunately, the vast majority of newly enfranchised people don't know about their restored voting rights. We're working alongside the Campaign Legal Center to convey a simple message across the state: A felony conviction does not always mean you lose your right to vote.
Mississippi is one of only four states to impose lifelong voting bans for people convicted of certain felonies. The law is a relic of the state's 1890 Constitution, a Jim Crow-era document designed specifically to disenfranchise African Americans following Reconstruction. It's still having its intended effect: One in six black adults in the state – and a total of more than 200,000 Mississippians – can't cast a ballot. The law even allows legislators to decide, on an individual basis, whose rights to restore.
We filed a lawsuit against the state to overturn this discriminatory law on the grounds that it violates the U.S. Constitution, including the 14th Amendment because it arbitrarily grants or deprives citizens of the right to vote and was intended to discriminate on the basis of race.
We helped secure the Voting Restoration Amendment. The statewide ballot initiative (Amendment 4) would reinstate the voting rights of 1.4 million Floridians who have been convicted of a felony at some point in their lives. These people – disproportionately people of color – have paid their debt to society and yet the state continues to silence their voices.
We've filed an amicus brief, along with The Sentencing Project and the NAACP Legal Defense and Educational Fund, in a lawsuit challenging Louisiana's felony disenfranchisement policy. Our brief demonstrates how Louisiana's felony disenfranchisement laws are rooted in racial discrimination.
We also worked successfully to pass legislation in 2018 to restore voting rights to thousands of Louisiana residents who have been on probation or parole for five years.
Teaching Tolerance
Teaching Tolerance's Voting and Voices Project supports educators as they register and motivate young voters and encourage students of all ages to become empowered and enthusiastic voting advocates. We're providing educators with a pledge for students to take home to their families to commit to using their voices and votes in the 2018 election. In partnership with Rock the Vote, the project will also offer Democracy Class as a resource for teaching about voting as well as resources for facilitating student-led voter registration drives.
SPLC on Campus
We're providing information and resources to help our SPLC on Campus clubs implement voting registration drives at their college or university.


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Last weekend was pretty bad, I wasn't feeling well for most of the weekend but did manage to make my tax and vet appointments. So at least the important stuff got done.
By Monday I was feeling well enough to go to work so we were also able to go to the National Geographic talk we had tickets for. This one was by a guy who has done several polar expeditions, across both the north and south poles. It was interesting to hear him talk about his motivations and see the video of how quickly the 2-3 foot thick pack ice moves in the north. But it wasn't quite as engaging and amazing as the one on the ocean health. I think part of that is that I love the ocean in a way that I just can't engage with the cold and danger of the poles, even with movies like March of the Penguins. The critters are cute, and their lives are hard and getting harder. It they're not the ocean.
Anyway, we still enjoyed the talk and I was fully engaged the whole time in spit of how tired I was. We'll definitely be back for more.
Posted via LiveJournal app for iPhone.


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text: Endthefed 0 Report post Posted March 11, 2016 <Vigil> is a semi-hardcore Alliance raiding guild on Sargeras-US. The majority of the guild has been playing since classic and the core group has been raiding together since Burning Crusade. We are looking for raiders, veteran or new, who are seeking a structured raid environment that pushes progression content on a 3 day per week schedule.Our Goals1) Maintain a semi-hardcore raiding guild focused on longevity. This guild was created based on friendships made in hardcore raiding and the guild will continue to thrive as long as World of Warcraft continues to exist.2) Conduct raids in a professional, organized, coherent manner. The leadership collectively brings over 20 years of hardcore raiding experience. What we enjoy most about raiding is the organization of the raid, adapting to difficult encounters, and celebrating after a difficult boss kill. Most of our trials comment on the speed of our raid. This is not a guild that takes 10-15 minutes between pulls. Strategy is done before raid on the forums and raid time is used wisely.3) Establish a community. We may be the only semi-hardcore progression guild with a minecraft server. We have a Sunday alt run, mumble is always active, and we play other games such as League of Legends or Heroes of the Storm. The average age of the guild is mid 20s with a few 30-40 year olds. Also, we have a really awesome website that looks great on mobile.Current NeedsCore positions are available for the following classes. All exceptional players are considered. We keep a lean roster of 23-25 raid members (including trials).Restoration Shaman or DIsc PriestMage Warlock HunterShadow Priest ProgressionT17: 7/7M Highmaul 10/10M BRFT18: 13/13M Hellfire CitadelRaid TimesTuesday 6-10pm CSTWednesday 6-10pm CSTThursday 6-10pm CSTLoot RulesWe use a loot council system and award loot based on attendance, performance, and attitude. We know how important loot is to player and guild advancement and take great care with awarding loot. Contact InformationWebsite: http://www.sargeras.usTwitter: www.twitter.com/Vigil_RaidingBattle.net IDs: Korson#1574 Tjuice#1677 Draconuss#1295 Quote Share this post Link to post Share on other sites


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The Secret Weapon of The 21st Century
In my conversations with some of the world's top experts I kept hearing this one idea come up again and again.
It seemed as if almost every guest I interviewed on The Science of Successkept bringing up the same key skill necessary for success in today's world.
That skill is self awareness.
In fact, I kept hearing self awareness crop up so frequently in my interviews with experts, neuroscientists, and psychologists that I sought out a leading research psychologist who specializes in self awareness — Dr. Tasha Eurich.
She called self awareness "The Secret Weapon of the 21st Century."
Self awareness underpins nearly every other skill. If you're self aware, you can grow, improve, learn, and get better. If you're not, you usually plateau and end up stuck.
Here's how Dr. Eurich puts it:
"The reason self awareness is the meta-skill is basically our self-awareness sets the upper limit for so many of the skills that we need to be successful in the world right now. Things like communication skills, influence, emotional intelligence, collaboration. We can only be as good at each of those things as we are self-aware. The other thing I call it sometimes is the secret weapon of the 21st-century. So many people think they're self-aware, but they actually aren't. So people that work on it are the ones that really I've seen reap the rewards time and time again." — Dr. Tasha Eurich
Are You Self Aware?
According to Dr. Eurich's research — 95% of people think they are self aware, but only 10–15% of people actually are.
That's pretty scary stuff. The even crazier thing is that those who have the least self knowledge are often the least aware of it.
The good news is that research also shows that self awareness is an "infinitely learnable skill" according to Dr. Eurich.
So what happen when you don't have self awareness?
- You don't see yourself objectively.
- You don't understand your own strengths, and much more importantly your own weaknesses.
- You aren't aware of your own cognitive biases.
- You aren't aware of the thought patterns and mental addictions that prevent you from achieving what you want.
- Deluding yourself.
- You reject criticism and feedback because you don't like it
- You reject ideas because you don't like them.
- You make blame and excuses for your failures instead of taking responsibility for them
- You don't know your own limiting beliefs.
- You don't recognize patterns of self sabotage that repeatedly manifest in your life.
Developing Self Awareness
You have to know your own strengths and weaknesses.
Ray Dalio, the billionaire founder of the hedge fund Bridgewater, created an incredible book called Principles that teaches the philosophy of how he views the world. Self knowledge and the search for objective truth are the core ideas of his work. For some bonus points you can also view his TED Talk here.
Here's a sample:
"Let's imagine that your goal is to have a winning basketball team. Wouldn't it be silly to put yourself in a position that you don't play well?If you did, you wouldn't get what you want. Whatever your goals are, achieving them works the same way. If you see that you are not capable of doing something, it is only sensible for you to have someone else do it. In other words, you should look down at you and all the other resources at your disposal and create a "machine" to achieve your goals, remembering that you don't necessarily need to do anything other than to design and manage the machine to get what you want.
If you find that you can't do something well, fire yourself and get a good replacement! You shouldn't be upset that you found out that you are bad at that — you should be happy because you have improved your chances of getting what you want. If you are disappointed because you can't be the best person to do everything, you are terribly naïve because nobody can do everything well. The biggest mistake most people make is to not see themselves and others objectively." — Ray Dalio
Accepting When You Make Mistakes
We've heard it from a billionaire, now let's see what a research psychologist who's studied tens of thousands of people has to say. Here's a paraphrased passage from one of my favorite books of all time — Mindset by Carol Dweck.
"Instead of trying to learn from and repair their failures, people with a fixed mindset simply try to repair their self esteem…by assigning blame or making excuses…You can still be in the process of learning from your mistakes until you deny making them." — Carol Dweck in Mindset
That last line is extremely important. The only way to learn from your mistakes is to actually accept that you made them. To be self aware enough to say "yeah, I screwed up" and to take responsibility for it.
Without self knowledge, you are constantly ignoring feedback from reality. Carol Dweck explains this further in a recent interview.
"If you're looking to repair your self-esteem, maybe you're looking for people who did worse, maybe you're looking to place the blame, maybe you're looking to deny the failure, in any of those cases, you're not going to be better off going forward.
Neuroscience research shows that when people are in a fixed mindset, the part of their brain that processes errors is hardly active. They are just turning away from that error as quickly as possible. As a result, they're not correcting the error at the next opportunity as much as people in a growth mindset.
In a growth mindset, that area of the brain is on fire, it's just super active, they're looking at the error, they're processing it, they're learning from it and they're correcting it." — Carol Dweck
The resounding lesson taught by everyone from Navy Seals to ancient stoic philosophers is clear — take responsibility for your own mistakes — be self aware that you screwed up and own it — that's the only way to create a learning opportunity.
Tools To Build Self Awareness
So what can you do to improve your self awareness?
Here are a few simple evidence based strategies you can use starting right now.
- Meditate. Yeah, everyone says meditation. That's because there is a ton of science about how it really does work. One of the first things meditation teaches you is how to be aware of your own thoughts and biases. That's a key step in the journey of self awareness and self knowledge. This podcast "The Skeptic's Guide To Meditation" is a great place to start.
- Develop a Growth Mindset. Carol Dweck touched on this earlier — and goes much more in depth in this interview as well as in her book. Developing a growth mindset gives you permission to look at your own mistakes and shortfalls without ego or defensiveness getting in the way. A growth mindset gives you the ability to objectively judge your own abilities. This is a cornerstone of self knowledge.
- Journal. Journalling helps put thoughts on paper, clear up your thinking,develop self awareness, and lets your return later to assess your thought process.
- Read up on rationality and cognitive biases. Check out people like Charlie Munger who speak about mental models and cognitive biases so you can learn to spot them in your own thinking.
- Check out this interview with Dr. Eurich to learn more. She's an expert in self awareness after all. Listen to that episode, check out some of her work, and learn the strategies she recommends for becoming more self awareness.
So there you go — this is the first step on the journey towards rationality and clear thinking — start developing your self awareness!
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