Sample Results From Member Downloads. There are many new features added with 12. Locate the following file, make a backup copy, and open with a text editor location might be slightly different if you chose a different data directory during install. Testing times for one application have fallen from 6 man-days to a few hours. . This is a problem for any company that has a mandated email naming convention which includes non-alphanumeric characters as our example, server-admin email.
I am trying to use Kodo in a standalone application and obviously need it to recognize the license key. I have no second chance to follow your tips. I would then complete the edit on my fully licensed version of the software. An individual who holds more than one classroom teaching certificate may meet the three-year experience requirement with any three years of classroom teaching experience. Anybody in a local network can connect to this pool as long as at least a single license is available. Since, it is always better to have a configuration higher than the minimum requirements. To use this seat license, user does not require any key.
Everything works fine until I try to create the mappings with 'mappingtool -p jdo. Certificates are issued in a wide variety of titles in each of these three major categories. It is required when you want to install a seat license on your machine. Not sure what I need to do next. It basically works on Windows 7 with service pack 1. I am trying to use Kodo in a standalone application and obviously need it to recognize the license key. Flexible, professional and approachable across the whole team I would certainly recommend them to other clients.
What is the link to download qtp 10 crack version?
How to restrict concurrent license usage among various groups in an organization? Alternative programs also allow you to be employed as a teacher while in the program. Suddenly after reboot all runbooks fail on all. My license is expired and i need to do some urgent task, 465 Average Download Speed, or registration number for a piece of software. Please explain with examples if Possible. You may have several groups in an organization using the same concurrent license server. We have successfully upgraded it to V-12 without any issues and has been using it.
But when starting the uft i'm presented with a license error, 0 License Key may also include a crack? How to check-out and check-in a commuter license? Step 10 You will be taken to your Dashboard. No problem with their 2nd attempt or any other usage. Concurrent License: This is also known as floating. You will need to specify your new admin password, load your licenses, and redo your configuration. Things like lost passwords and corruption. Now, enter the email credentials password mailpw into the following entry key toward the top of the file. This means that any licenses that already exist for 12.
LicenseException: No product license key was found. Install was successful but when I try to give the Valid License Name and establish the connection to the License Server it just gives me an exception that the license server is not found. Additionals from qtp license key free Does anyone have insight how to solve this. If you search a download site for Uft 12 Keygen, this often means your download includes a keygen. Do I need to uninstall concurrent license and then install seat license? Regards, Jake Hi Jake, I don't think the problem is getting Kodo to recognize the license outside the web application I am also working on a standalone application. Although , I also have an install on my local machine which is able to connect to the License server and work successfully.
Step 3 Check your Inbox and confirm your email. Enter Data and Click Sign Up Step 2 In the next screen, you will see a message registration was successful. I try to use ant task for enhancement,here is my build. I didn't change settings environment variables,. So stay tuned for upcoming interesting articles in the queue.
Solved: UFT 12.5 Concurrent license working with UFT14?
Can anyone please help me in. For instance, a teacher who holds the Childhood Education Grades 1-6 and English Grades 7-12 certificates could satisfy the experience requirement for both certificates by teaching for three years in an elementary classroom, three years as a secondary English teacher, or a combination of both. Hi Ankur, My Question is reg Commuter Licence: I have generated the commuter license for remote machine using locking code. This project requires a Unified Functional Testing license. Please contact us through chat support so that we can help you out. I have worked on QuickTest Professional since version 8. A license can be checked-out for a max duration of 180 days, post which it will automatically be returned to the license pool.
Thsi issue would require us to check your serial number which i would not advise you to share on Forums. Before you install the trial license, make sure that you have gone through all the. The word keygen means a small program that can generate a cd key. Could you please help me in understanding the issue. Let me know how to solve my issue. Installing a seat license and making it stick for all users requires it to be installed with administrator rights otherwise, it would need to be installed under each user that accesses the physical machine. Many downloads like Uft 12 may also include a crack, serial number, unlock code, cd key or keygen key generator.


Since its inception Bollywood has witnessed several glittering stars!
While many beautiful faces just vanished and were forgotten, many were etched on our hearts forever. With their flawless beauty and vibrant on-screen presence, these stars cannot be forgotten.
Have you ever wonder what they look like today? Well, take a look at some of these actors and their latest looks.
At the age of 22 Actress Mandakini made her debut with Raj Kapoor's film, 'Ram Teri Ganga Maili and became a overnight star.' Now, she runs a centre for Tibetan medicine.
Famous for her cult classic song 'Jumma chumma', Kimi Katkar is still remembered for her latkas and jhatkas!
Remember Sunil Shetty's beautiful bride in the film 'Border'? Yes, the bride was Sharbani Mukherjee, cousin of actresses Kajol Devgn and Rani Mukherjee Chopra!
The child actor who played the younger version of Rani Mukherjee in the film 'Black', has grown into an elegant lady!
Better known as Rita from Rajshri's blockbuster film 'Hum Aapke Hain Koun' ! Sahila played the role of Rita, a young vibrant girl, who was after Salman Khan in the film.
Do you remember the deaf and dumb kid from Govinda's 'Hatya'? The kid is a girl and has grown into a beautiful young girl.
Pooja made her Bollywood debut as a bubbly little girl with Jackie Shroff-starrer 'King Uncle'. Later, she essayed a role of a teenager Chutki in 'DDLJ'.
The beauty queen created quite a stir with her bold avatar in 'Aashiq Banaya Aapne' and in 2010 she went on a three years sabbatical.
Sandali Sinha rose to fame with her critically acclaimed film 'Tum Bin'. However, she didn't do many films post Tum Bin!
South actress Keerthi Reddy made her foray into Bollywood opposite Abhishek Bachchan in 2000.
The once chiselled actor Fardeen Khan is famous for his films like 'Jungle' and 'No Entry'. You may take some time to recognise the actor in his new looks.
Gracy Singh made her big screen debut with the Oscar-nominated film, 'Lagaan.' Now, the actress is back to TV and playing the role of goddess in one of the shows.


Image Boxes (and Text Boxes) should have a "snap" alignment feature allowing them to easily align with others on the page.
When importing images, they often will not show at the bottom of the image import dialog box unless you open it sometimes three or four images wide. This needs correction.
When you single click on an image in the image import dialog box area where the images show, it will often jump to the very top of the image dialog box. Very annoying when you have a large number of images showing.
Footers with page numbers should auto align based on left or right pages unless otherwise specified even when duplicating a layout.
Text in text boxes should reformat for size when scaling book size down from Large Landscape to Small Landscape.
Create your own background patterns and save to available patterns like page layouts.
Image import area could allow for separation of images into folders or chapters.
I'm having serious issues with the auto-flow text feature. I wish I could just "break" the flow and format it myself. The program runs slow as tar while it's trying to flow the text and almost never gets it right.
I've found some annoying bugs. When you click undo, it may swap the photos back to their original place, but not their original placement or magnification. So it's really an undo completely. Add to this that once you add/import a photo into a space on your layout, it deselects/loses focus on that picture and pops someplace else. So if you import a picture and it's rotated incorrectly, you'd naturally and immediately want to click the rotate button. But the cursor focus has changed, so you end up rotating some other random photo. Then if you hit undo, it'll re-rotate the correct way, but loses the placement within the frame and the magnification. It's a basic workflow problem to not expect someone to want to work on the thing they JUST imported.


text:
A work-life balance refers to a sustainable and effective distribution of self between one's work and life obligations, suggesting a clear separation between "work" and "life" with minimal spillover of one into the other. It refers to the ability to effectively attend to one's work responsibilities and one's personal responsibilities, with adequate reserves to balance it all. But consider this: More than 40% of psychiatrists believe their careers in medicine are "a calling." Thus, we tend to have a deep connection to our work.1 This may be both a blessing and a curse: Data suggest this viewpoint may play a protective role in our resilience. However, it may also be difficult to separate work from our other senses of self. When faced with tahe age-old question, "Do you live to work or do you work to live?" it seems many of us would reply affirmatively to both options.
The idea of creating and maintaining balance has always been difficult and even more so during the COVID-19 pandemic. The drawing of a line between work and home life became impossible as we were suddenly thrust into telepsychiatry mode, without any clear ways to apply our office-based approaches and boundaries. On top of that was the need to simultaneously manage distance learning for our children, negotiate infection control, deal with pandemic fears, and so on.
The corporate world has pushed to reconceptualize this as work-life integration rather than balance. The word integration lessens the need to separate oneself into disparate parts to address various areas of life. In addition, the emphasis of integration over balance suggests that you are a whole person, with an identity and sense of wellness spanning, affecting, and being affected by all areas of work and non–work life. Because our profession necessitates maintaining clear boundaries for therapeutic and safety reasons, this integrative concept may best be applied in terms of how it affects our self-concept, by easing our need to find a way to split our identity. It also allows us the space to then prioritize ourselves and our health in all areas, knowing that when we are not feeling well in our personal lives, this greatly impacts our work life and vice versa.

redpajama_set_name: RedPajamaCommonCrawl


text:
Q: Nest can't resolve dependencies of the AppModule (?) After set a middleware I try to follow the documentation of nestjs  middleware and I have a problem on my nest application after adding and set a middleware.
I've seen the FAQ about the same common error from nestjs itself but still confused to solve this problem.
Error on my terminal
Error: Nest can't resolve dependencies of the AppModule (?). Please make sure that the argument Object at index [0] is available in the AppModule context.

Potential solutions:
- If Object is a provider, is it part of the current AppModule?
- If Object is exported from a separate @Module, is that module imported within AppModule?
  @Module({
    imports: [ /* the Module containing Object */ ]
  })

    at Injector.lookupComponentInParentModules (c:\codelab\nestlab\node_modules\@nestjs\core\injector\injector.js:230:19)
    at Injector.resolveComponentInstance (c:\codelab\nestlab\node_modules\@nestjs\core\injector\injector.js:184:33)
    at resolveParam (c:\codelab\nestlab\node_modules\@nestjs\core\injector\injector.js:106:38)
    at async Promise.all (index 0)
    at Injector.resolveConstructorParams (c:\codelab\nestlab\node_modules\@nestjs\core\injector\injector.js:121:27)
    at Injector.loadInstance (c:\codelab\nestlab\node_modules\@nestjs\core\injector\injector.js:52:9)
    at Injector.loadProvider (c:\codelab\nestlab\node_modules\@nestjs\core\injector\injector.js:74:9)
    at async Promise.all (index 0)
    at InstanceLoader.createInstancesOfProviders (c:\codelab\nestlab\node_modules\@nestjs\core\injector\instance-loader.js:44:9)
    at c:\codelab\nestlab\node_modules\@nestjs\core\injector\instance-loader.js:29:13

the problem is while I set AuthMiddleware on my AppModule constructor like this
  constructor(consumer: MiddlewareConsumer) {
    consumer
      .apply(AuthMiddleware)
      .forRoutes({
        path: '*',
        method: RequestMethod.GET,
      });
  }

and this my related code.
AuthMiddleware.ts
import { Injectable, NestMiddleware } from '@nestjs/common';

@Injectable()
export class AuthMiddleware implements NestMiddleware {
  use(req: any, res: any, next: () => void) {
    console.log('AuthMiddleware passed');
    next();
  }
}

app.module.ts
import { MiddlewareConsumer, Module, RequestMethod } from '@nestjs/common';
import { AppController } from './app.controller';
import { AppService } from './app.service';
import { AuthMiddleware } from './auth.middleware';

@Module({
  imports: [],
  controllers: [AppController],
  providers: [AppService],
})

export class AppModule {
  constructor(consumer: MiddlewareConsumer) {
    consumer
      .apply(AuthMiddleware)
      .forRoutes({
        path: '*',
        method: RequestMethod.GET,
      });
  }
}

Thanks for your attention & your help.

A: the Object is provider means that you're injecting using an interface (the MiddlewareConsumer one)
If you read the docs on middleware consumer, you'll notice that the
consumer: MiddlewareConsumer parameter
should be used in configure method, not in constructor
Also, do this to improve the type checking:
class AppModule implements NestModule {}

redpajama_set_name: RedPajamaStackExchange


Foot Locker just nailed it. In a new spot, the de facto no. 1 draft pick of the NBA Draft, Ben Simmons, gets some rookie advice from Officer D'Angelo Russell, and it's hilarious.
The comedy is because while the NBA prospect is seeking advice, Russell takes Simmons' cell phone and hurls it out of the window and into the ocean. The Lakers point guard is poking fun at the predicament he found himself in when cell phone footage he took of Swaggy P allegedly admitting to cheating on his then-fiancee Iggy Azelea made it onto the Internets.
Coupled with the fact that Iggy just broke up with Swaggy, via Instagram, and the forthcoming NBA Draft, the timing for this spot couldn't be more perfect.
Another commercial (watch on the flip) features Simmons awkwardly speaking to a faux-Karl Anthony Townes.
Both spots will air nationally on ESPN during the the NBA Draft on June 23 and are part of Foot Locker's "Approved" marketing campaign.
The real deal NBA rookie of the year, and Devon Booker, are in the cell phone commercial, titled "It's Real Now," which you can watch below.


text:
The Old Philadelphia Presbyterian Church is a historic church in Quincy, Florida. It is located five miles north of Quincy, off SR 65 on County Road 272. On February 24, 1975, it was added to the U.S. National Register of Historic Places.

References

External links

 Gadsden County listings at National Register of Historic Places
 Florida's Office of Cultural and Historical Programs
 Gadsden County listings
 Gadsden County markers
 Old Philadelphia Presbyterian Church

Churches in Gadsden County, Florida
Churches on the National Register of Historic Places in Florida
Presbyterian churches in Florida
National Register of Historic Places in Gadsden County, Florida

redpajama_set_name: RedPajamaWikipedia


I'd like to have your point of view about image displays via odoo manifest file. (readme file).
For me, making promotion of modules it's a very important point for the OCA and images are very good to understand very quickly, how a module works, and which features are added.
I do not understand how to write the path of the image to make it displayed correctly in all supports. (that are : github, odoo apps website, odoo-code-search website and 'local modules' in odoo itself).
The OCA template is not very explicit on that point (line 26), but maybe I missed something.
It seems that there is no ideal solution for the time being.
We should select a github compatible solution, but none works with odoo apps and odoo-code-search.
I propose you #1 syntax because it's the most natural and logical. (simple relative path).
Alternatively, we can select #5 that works in 'local module' menu too.


Spanish poet and dramatist, a talented artist and a member of the 'Generation of 1927', a group of writers who advocated avant-gardism in literature. Among García Lorca's best-known plays is Blood Wedding (1933), a story of a bride who runs away with a previous lover, and is subsequently murdered by her husband. After a period of great creative activity, García Lorca was shot by Falangist soldiers in the opening days of the Spanish Civil war. In both his drama and poetry García Lorca balanced between the traditional and the modern, between mythology and contemporary cultural trends.
Federico García Lorca was born at Fuente Vaqueros, a village on the banks of the River Genil, a few miles from Granada. His father, Federico García Rodriguez, was a prosperous farmer. Vicenta Lorca Romero, García Lorca's mother, had been a schoolteacher before becoming Federico's second wife. To the Moorish city of Granada, in which García Lorca was raised, the author returned several times in his poetry and drama.
García Lorca first read law at the University of Granada, but later entered the University of Madrid. At the same time he also studied music. In the 1920s García Lorca collaborated with Manuel de Falla, becoming an expert pianist and guitar player. In 1919 he moved to Madrid, where he lived at the Residencia de Estudiantes, the intellectual center of the town. His friends included the writers Juan Ramón Jiménez and Pablo Neruda. With the Catalan painter Salvador Dalí and the film director Louis Buñuel he worked in different productions. Dalí and Lorca had met in 1923. From the beginning, Lorca was fascinated by the young Catalan's personality and looks. Also Dalí had admitted that Lorca impressed him deeply. "The poetic phenomenon in its entirety and 'in the raw' presented itself before me suddenly in flesh ad bone, confused, blood-red, viscous and sublime, quivering with a thousand fires of darkness and of subterranean biology, like all matter endowed with the originality of its own for." (from Secret Life) When Buñuel and Dalí made their famous short film Un Chien Andalou (1928), García Lorca was offended: he thought that the film was about him. Lorca's friendship with Dalí inspired a poem, a defense of modern art and at the same time an expression of homosexual love.
Before the publication of his first collection, García Lorca was already known as a poet, especially through his recitals of his texts. As a writer García Lorca made his debut with LIBRO DE POEMAS (1921), a collection of fablelike poems. His first book had been a travelogue, IMPRESIONES AND PAISAJES (1918). In 1923 García Lorca earned a degree in law and turned his back to his university career.
In 1927 García Lorca gained fame with his romantic historical play Marina Pineda, where the scenery was constructed by Salavador Dali and the distinguished actress Margarita Xirgu played the heroine. By 1928, with the publication of PRIMER ROMANCERO GITANO, he was the best-known of all Spanish poets, and leading member of the 'Generation of 27', which included Luis Cernuda, Jorge Guillen, Pedro Salinas, Rafael Alberti, and others. In 1929-30 García Lorca lived in the city of New York, on the campus of Columbia University. Unable to speak English, he suffered a deep culture shock. However, in his early letters to home he expressed his enthusiasm about contemporary American plays. His suicidal mood was recorded in posthumously published POETA EN NUEVA YORK (1940, Poet in New York), in which he praised Walt Whitman. The poet condemns the frightening, physically and spiritually corrupted city, and escapes to Havana to experience the harmony of a more primitive life.
García Lorca's experiments in the theater – he rebelled against the realistic theater of the middle class – involved such puppet plays as TÍTERES DE CACHIPORRA (1949) and EL RETABLILLO DE DON CRISTÓBAL (1938). In 1933 he wrote two surrealistic dramas, EL PÚBLICO, an attack on commercial theater and the entire social order, and ASÍ QUE PASEN CINCO AÑOS, an allegory of lost time. Mariana Pineda (1928) was García Lorca's only historical drama. Its central character, a Spanish national heroine, was a historical figure – she was executed for embroidering a revolutionary flag. The play portrays her as a martyr to liberty and love: "I am freedom because love wanted it so; / Pedro! the freedom for which you left me. / I am freedom stricken by men / Love, love, love and eternal solitudes." EL AMOR DE DON PERLIMPÍN CON BELISA EN SU JARDÍN, writtenin1931, was a farce, but it also ended in death. "Take me by the hands, my love, / for I come quite badly wounded, / so wounded by love's going. / So wounded. / Dying of love!" The first version of the surrealistic drama EL PÚBLICO (The Audience) Lorca finished in 1939, but returned to it again in 1936. The play remained unpublished until the 1970s – it dealt with homosexual love, a taboo subject on the Spanish stage for many years.
Blood Wedding, the first part of García Lorca's famous rural trilogy, was performed in 1933. The love triangle, blending drama and poetry, closely resembled a classical Greek tragedy, in which death hovers over the whole play. Yerma, the second part, performed in 1934, portrayed a deadly conflict in a barren marriage. The heroine strangles Juan, her husband, who do not understand her yearning to love and bear children. The House of Bernarda, written just before García Lorca's death in 1936 and published in 1945, depicted a tyrannical mother, Bernarda Alba, and her daughters. The youngest daughter commits suicide over her lover, Pepe el Romano, who is engaged to Angustías, the oldest daughter.
García Lorca's central themes are love, pride, passion and violent death, which also marked his own life. The Spanish Civil was began in 1936 and García Lorca was seen by the right-wing forces as an enemy. The author hid from the soldiers but he was eventually found. An eyewitness has told that he was taken out of a Civil Government building by guards and Falangists belonging to the 'Black Squad'. García Lorca was shot in Granada on August 19/20 of 1936 without trial. The circumstances of his death are still shrouded in mystery. He was buried in a grave that he had been forced top dig for himself. Accroding to some sources, he had to be finished off by a coup de grâce. One of his assassins later boasted, that he shot "two bullets into his arse for being a queer". Most probably, García Lorca wrote under pressure his last words on a note for a member of the 'Black Squad': "Father, please give this man a donation of 1000 pesetas for the Army." Don Federico, his father, carried the note in his wallet for the following years. He died in voluntary exile in New York.
For further reading: García Lorca by E. Honig (1944); Lorca and the Spanish Poetic Tradition by J.B. Trend (1956); The Theatre of García Lorca by R. Lima (1963); Lorca: Poeta maldito by F. Umbral (1968); Lorca and the Spanish Tradition by J.B. Trend (1971); García Lorca: Playwright and Poet by M. Adams (1977); The Assassination of Federico García Lorca by I. Gibson (1979); Lorca's New York Poetry by R.L. Predmore (1980); Federico Carcía Lorca by R. Anderson (1984); Federico García Lorca by F.H. Londré (1984); Federico García Lorca by I. Gibson (1989); Lorca's Late Poetry: A Critical Study by A.A. Anderson (1990); Audience and Authority in the Modernist Theater of Federico García Lorca by C.C. Soufas (1996); Encyclopedia of World Literature in the 20th Century, ed. by Steven R. Serafin (1999, vol. 2) – Other writer in the Spanish Civil war: Ernest Hemingway, André Malraux, George Orwell, Langston Hughes – In exile during the Civil War: José Ortega y Gasset – Suom.: García Lorcalta on myös suomennettu valikoima Andalusian lauluja.


Golf Club Konopiště is a civic association operating its activities in the Golf Resort Konopiště facility. It is a member of the Czech Golf Federation with the membership registration number 044. The club members can be both Czech nationals and foreigners. The club is open not only for adult golfers but also for juniors.


Continuing my discussion of preproduction, I talk about what that can be like in the AAA industry. Normally, there is a greenlight process where a playable demo has to be made. Watch the video to find out more!


text:
"Gei Oni" Awarded Best film in New-Delhi
Delhi International Film Festival closed last night and to my amazement my film "Valley of Strength" (Gei Oni) based on Shulamit Lapid's novel won the top award -
"Best film" of the World Cinema section.
My Chinese film
This December my new film "New Voice" will premiere in Beijing China. I wrote the screenplay and directed it, but it is entirely in Chinese and the cast is all Chinese too. Here is how it started...
"New Voice" - the Premiere
"New Voice", A Co-production of the Israeli Embassy and The National Academy of Chinese Theater Arts written & directed by Dan Wolman veteran Israeli film director has been premiered at The National Academy of Chinese Theater Arts, last week, December 6th .
An interview about "Gei Oni" in New York
A few days before the opening of "Gei Oni" at the Quad cinema in New York (July 13th 2012) - Aaron Herman met me in central park, took a little camera from his case, set it up on a tripod and interviewd me. He is a "low bugetor"
like me.


Henric Johansson is an artist with a deep and passionate appreciation for beauty in the natural world and within the human spirit. This enthusiastic and sensitive joy is reflected strongly in his artwork. His work is executed in a vibrant, impressionistic style, which - while still retaining its representational roots, incorporates abstract elements, resulting in a uniquely contemporary union.
"Nature has provided me with a spiritual compass in life, and observing it and drawing it has always been, for me, the most rewarding way to honor that reverence. I believe that the knowledge gained through quiet observation, study and meditation is just as important as the actual application of paint to canvas.
Rather than go out into the landscape with the notion of 'seeking' a painting, I find that the stronger ideas appear to me, or are given to me, when I am hiking, making a fire, fishing, or meditating – when I am utterly 'present'; when I am the student, the observer, and the recipient; when I 'listen' rather than impose my will.
For me a painting should be evidence of a process of observation and acquired knowledge rather than a concluding statement. The process, not the product, is the closest I have come to 'living life' in its truest sense.
Give support to The Elevator process..."One thought away..."
Anyone can help transform the vision into something real by making donations.
Support andhelp to finance the "The Elevator "project by filling out the form below.


text:
USA Weekly / People
Honey maker is a money maker
By Todd Balazovic (China Daily)
Alia Malik is leading the charge for "social enterprise" in China by providing all-natural products to customers and long-term training and better-than-market prices to their producers. [Provided to China Daily]
Development aid gets a personal and practical touch that enriches those who take part
Profit is not a dirty word for Alia Malik, who is turning a penny or three as she helps the less fortunate.
The co-founder and director of Shangrila Farms, which sells homegrown organic honey, coffee and skincare products, has spent the past two years building an enterprise that gives rural people the chance to be involved in fair-trade business.
By nurturing small communities in Yunnan province, Malik is leading the charge for "social enterprise" in China by providing all-natural products to customers and long-term training and better-than-market prices to their producers.
"It's a business. It is trying to exist so it can turn profits while also trying to achieve social and environmental outcomes for the communities we work with," Malik says.
The 29-year-old New York native first came to Beijing in 2003 to visit her family, where her father was working with the United Nations Development Program.
While she had traveled to many developing countries when she was studying, China stood out, she says.
"It was just so interesting, the people, the places. At the time it was developing so quickly as well, unlike anything I've seen before. You could go to sleep one day and the next the entire street outside your apartment would be completely transformed."
Malik spent a year studying Chinese in Beijing before moving to Britain, and gained a master's degree in development studies at Cambridge University. It was there that she first came across the idea of social enterprise.
She was impressed with the idea of combining business with philanthropy, but skeptical that it could work.
After gaining her degree, Malik spent several years working on various research and development projects in countries such as Kenya and Uzbekistan, providing aid to rural communities in need.
She saw the immediate benefits of services, such as providing tablets for clean drinking water or insect nets to prevent malaria. But she also began to realize how weak traditional development strategies were.
"With rural government projects (there is) a planned timed limit. There's a budget, the phasing in and phasing out stages. Whether it's for a year or five years, at some point the project closes and you don't know what happens."
Often the benefits of such projects ebb away, she says.
"This is what got me interested in the model of social enterprise - to create something that exists as more than just a project. In theory, you should be able to have stable benefits for the communities while also surviving as a business."
Malik returned to Beijing in 2008 to pursue her newfound enthusiasm and began working with organizations focused on helping jump-start such businesses in the capital.
However, there was little interest by entrepreneurs in businesses that did not focus on profits.
At the same time, Malik and her sister Sahra were working to raise money for the Yunnan Mountain Heritage Foundation, an NGO whose focus is alleviating poverty. Malik's mother runs the NGO in southwestern Yunnan province. The two sisters' efforts centered on marketing products generated by the farming communes involved with the foundation.
For Malik, it was mainly about raising funds by selling organic honey produced by beehives in one of Yunnan's sprawling nature reserves.
For her sister, it was about taking advantage of the flourishing coffee crops of the subtropical region.
"There was a lot of satisfaction when I saw that first jar of honey, when I saw something that was just an idea become reality," she says.
Seeing the lack of social enterprise business in China and the opportunity to capitalize on the products already being produced by their mother's NGO, Malik quit her job and with her sister decided to set up Shangrila Farms in Beijing in 2009.
That led her from theorizing about social enterprise to pulling up her sleeves and doing it.
"Eventually I decided just to take the plunge."
The two-person operation started simply, with neither taking a salary, but has slowly begun to prove successful.
Late last year the pair appeared on Chinese television talking about their efforts with Shangrila Farms. It was after that appearance, Malik says, that they knew they were onto a winner.
Following their 10-minute TV appearance, they were overwhelmed with Internet orders, and the following day their stock sold out.
"China is becoming increasingly socially aware. People are volunteering, and more people are working in environmentally and culturally conscious organizations," Malik says.
"But until you actually see success you don't know if your concept is actually meaningful to people. I think that appearance showed that ours was."
With their products now being offered in more than 50 shops in Beijing and Internet orders booming, Malik says they are eyeing locations in Shenzhen, Guangzhou and Shanghai.
"I feel like we've achieved a loyal customer base. The people who are buying our products love our products. Now we want to hear more feedback from the Chinese. We want to have a broader outreach to the Chinese consumers."
And as her business grows, so does Malik's drive to benefit rural areas.
Using their success, Malik says they plan to train more people in Yunnan in beekeeping to give them the skills needed to earn a reliable income.
"Business doesn't have to be just about money. If more businesses can balance profits and people, everyone can benefit."
China a magnet for global filmmakers
New irrigation tech coming to China
US firms eye China mining sector
Economic slowdown a boon to Blackstone
Hot spot: This is home of China
Amazon's new foray into China
Gates pact to boost health, agriculture
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Billionaires in China, RMB's appreciation , Shortage of life-saving heart drug, 9/11 anniversary, Biden talks China,
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My pre-order for Garth Brooks' new album was placed over the weekend and as a result, I received downloads of "People Loving People" as well as a second track from the new album "Send 'Em on Down the Road." While there is no indication that this has been set to be a single, I wanted to give it a review as it is the second taste of what is to come on the new album.
This is a song much closer to what we expected from Brooks when his comeback was officially announced. It is not the up-tempo nature of "Ain't Goin' Down Till The Sun Comes Up." It's also not a song with a message like "People Loving People." It's actually a much slower paced song, but there's a lot of substance.
The song is about parenting and what parents can and cannot do for their children. It starts out with Brooks singing as he remembers the growing pains of childhood. These aren't major struggles that a lot of people won't be able to relate to…they're the things which every kid growing up has to go through, which makes the song very poignant. As the song progresses into the second verse, the singer realizes the same things about parenting that his father knew, which are related in the chorus.
The production in the song is beautiful. The vocal work is beautiful, everything we'd expect from Brooks, everything we hoped for when his comeback was announced. It instills a lot of confidence in what's to come on Brooks' new album. You can hear in the song so much of what made Brooks so popular in the 90s.


text:
package org.apache.hadoop.mapred;

import java.io.IOException;

import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.compress.CompressionCodec;
import org.apache.hadoop.io.compress.CompressionCodecFactory;
import org.apache.hadoop.io.compress.SplittableCompressionCodec;

/**
 * An {@link InputFormat} for plain text files. Files are broken into lines.
 * Either linefeed or carriage-return are used to signal end of line. Each line
 * is divided into key and value parts by a separator byte. If no such a byte
 * exists, the key will be the entire line and value will be empty.
 *
 * @deprecated Use
 * {@link org.apache.hadoop.mapreduce.lib.input.KeyValueTextInputFormat}
 * instead
 */
@Deprecated
public class KeyValueTextInputFormat extends FileInputFormat<Text, Text>
  implements JobConfigurable {

  private CompressionCodecFactory compressionCodecs = null;

  public void configure(JobConf conf) {
    compressionCodecs = new CompressionCodecFactory(conf);
  }

  protected boolean isSplitable(FileSystem fs, Path file) {
    final CompressionCodec codec = compressionCodecs.getCodec(file);
    if (null == codec) {
      return true;
    }
    return codec instanceof SplittableCompressionCodec;
  }

  public RecordReader<Text, Text> getRecordReader(InputSplit genericSplit,
                                                  JobConf job,
                                                  Reporter reporter)
    throws IOException {

    reporter.setStatus(genericSplit.toString());
    return new KeyValueLineRecordReader(job, (FileSplit) genericSplit);
  }

}

redpajama_set_name: RedPajamaGithub


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that did better than you .


With help from the staff at CDS Lakeshore Head Start, children will learn how plants grow by planting a seed to take home with them.
The Kids Activities at the Holland Farmers Market are fun, educational, hands-on activities designed for children ages 3-10. Activities are held every Wednesday morning from 9:30 am – 11:30 am from June 13 – August 29 and are free to attend. Each Activity kicks off with Market Story Time presented by the Herrick District Library. Children must be accompanied by a parent/guardian. Big thanks to our sponsor Higher Health Chiropractic!


text:
\section{Terminology and Abbreviations}

\begin{tabular}{p{1.25in}p{3.25in}}
$p({\textnormal{x}})$ & Training data distribution \\
$q({\textnormal{x}})$ & OoD data distribution \\
$p_\theta({\textnormal{x}})$ & Learned approximation of true distribution with parameters $\theta$. May be implicitly specified, i.e. a fully-observed density model \\
$q_\theta({\textnormal{x}})$ & Learned approximation of OoD distribution with parameters $\theta$. May be implicitly specified, i.e. via a GAN discriminator that learns $p(x)/q_\theta({\textnormal{x}})$ \\
\midrule
OoD Input & Out-of-Distribution Input. Invalid input to a ML model \\
Anomaly & Synonym with OoD Input \\
Epistemic Uncertainty & Variance in a model's predictive distribution arising from ignorance of true model parameters for a given input \\
Aleatoric Uncertainty & Variance in a model's predictive distribution arising from inherent, irreducible noise in the inputs \\
Predictive Uncertainty & Variance of a model's predictive distribution, which takes into account all of the above \\
\midrule
MNIST & Dataset of handwritten digits (size: 28x28) \\
FashionMNIST & Dataset of clothing thumbnails (size: 28x28) \\
CIFAR-10 & Dataset of color images (size: 32x32x3) \\
\midrule
GAN & Generative Adversarial Network. See \cite{goodfellow2014generative} \\
FSGM & Fast Sign Gradient Method \\
WGAN & Wasserstein GAN. See \cite{arjovsky2017wasserstein} \\
VAE & Variational Autoencoder. See \cite{kingma2013auto, rezende2014stochastic} \\
Rate & $D_{\mathrm{KL}} ( q_\theta(z|x) \Vert p(z) )$ term in the VAE objective. Information loss between encoder distribution and prior over latent code \\
IWAE & Importance Weighted Autoencoder \\
GLOW & A generative model based on normalizing flows. See \cite{kingma2018glow} \\
\midrule
ODIN & Out-of-DIstribution detector for Neural networks. See \cite{liang2017enhancing} \\
VIB & Variational Information Bottleneck. See \cite{alemi2018uncertainty} \\
WAIC & Watanabe-Akaike Information Criterion. See \cite{watanabe2010asymptotic} \\
\midrule
AUROC & Area Under ROC Curve \\
FPR@95\%TPR & False Positives at 95\% True Positives \\
AP & Average Precision \\
\end{tabular}

\input{supplemental_gan}

\section{VAE Architectural Details}
\label{appendix:maf_vae}

We use a flexible learned prior $p_\theta({\textnormal{z}})$ in our VAE experiments, but did not observe a significant performance difference compared to the default mixture prior in the base VAE code sample. We use an alternating chain of 6 MAF bijectors and 6 random permutation bijectors. Each MAF bijector uses TensorFlow Probability's default implementation with the following parameter:

\begin{lstlisting}
shift_and_log_scale_fn=tfb.masked_autoregressive_default_template(
  hidden_layers=(512, 512))
\end{lstlisting}

Models are trained with Adam ($\alpha=1\mathrm{e}{-3}$) with cosine decay on learning rate.

\section{Discussion and Future Work}
Out-of-Distribution (OoD) detection is a critical piece of infrastructure for ML applications where the test data distribution is not known at training time. Our paper has two main contributions. The more straightforward one is a novel technique for OoD detection in which ensembles of generative models can be used to estimate WAIC. We perform a comparison to prior techniques published in OoD literature to show that it is competitive with past models and argue for why OoD detection should be done "model-free".

The other contribution is the intriguing observation that WAIC shouldn't work! It has been established in the literature~\cite{nalisnick2018dodeep, hendrycks2018deep} that likelihood alone is not sufficient for determining whether data is out of distribution. Simply put, regions with high likelihood are not necessarily regions with high probability mass. It is premature, however, to invoke this explanation as the sole reason generative models trained on CIFAR-10 fail to identify SVHN as OoD, as we show, \emph{e pur si muove}, that robust likelihood measures like WAIC \emph{can} distinguish the samples correctly. As we show in Figure~\ref{fig:radii_interior}, the maximum likelihood objective typically used in flow-based models forces the training distribution (CIFAR-10) towards the region of maximum likelihood in latent space. We hypothesize that these datasets are quite different, different enough that the SVHN images likelihoods can be very sensitive to the initial conditions and architectural hyperparameters of the trained generative models. The surprising effectiveness of WAIC motivates further investigation into robust measures of typicality which incorporate both likelihood as well as some notion of local volume, as it is clear that there is an incomplete theoretical understanding within the research community of the limits of likelihood based OOD detection.

Furthermore, the observation that CIFAR-10 is mapped to an annulus of radius $< \sqrt{3072}$ is in itself quite disturbing, as it suggests that better flow-based generative models (for sampling) can be obtained by encouraging the training distribution to overlap better with the typical set in latent space.

\section{Experimental Results}
\label{sec:experiments}

Following the experiments proposed by \cite{liang2017enhancing} and \cite{alemi2018uncertainty}, we train OoD models on MNIST, Fashion MNIST, CIFAR-10 datasets, and evaluate anomaly detection on test samples from other datasets. Source code for VAE and GAN experiments are located at \url{https://github.com/hschoi1/rich_latent}, and code for reproducing ODIN baselines are located at \url{https://github.com/ericjang/odin}.

We approximate likelihoods with two kinds of models, which we abbreviate as $p_\theta({\textnormal{x}})$ in Table~\ref{table:results_table}. For MNIST and FashionMNIST, we estimate $\log p_\theta({\textnormal{x}})$ using a 16-sample Importance-Weighted AutoEncoder (IWAE) bound, and for CIFAR-10, we compute exact densities via Glow \cite{kingma2018glow}. We follow the protocol as suggested by \cite{lakshminarayanan2017simple} to use 5 models with different parameter initializations, and train them independently on the full training set with no bootstrapping. For VAE architectures we also evaluate the rate term $D_{\mathrm{KL}} ( q_\theta(z|x) \Vert p(z) )$, which corresponds to information loss between the latent inference distribution and prior \cite{alemi2018uncertainty}. Glow uses a fixed isotropic Gaussian latent distribution, so we also devise a ``typicality-aware baseline'' that measures the euclidean distance $\left\Vert d \right\Vert $ to the closet point on a hypersphere of radius $\sqrt{3072}$. Finally, we report GAN-based anomaly detection in Appendix \ref{appendix:gan}.
We extend the VAE example code\footnote{\url{https://github.com/tensorflow/probability/blob/6dfeb4ba7a5164fe4617e4967b168640109221c9/tensorflow_probability/examples/vae.py}} from Tensorflow Probability~\citep{dillon2017tensorflow} to use a Masked Autoregressive Flow prior \citep{papamakarios2017masked}, and train the model for 5k steps: The encoder consists of 5 convoluton layers. The decoder consists of 6 deconvolution layers followed by a convolution layer, and is trained using maximum likelihood under independent Bernoullis. We use a flexible learned prior $p_\theta({\textnormal{z}})$ in our VAE experiments, but did not observe a significant performance difference compared to the default mixture prior in the base VAE code sample. We use an alternating chain of 6 MAF bijectors and 6 random permutation bijectors. Models are trained with Adam ($\alpha=1\mathrm{e}{-3}$) with cosine decay on learning rate. Each Glow model uses the default implementation and training parameters from the Tensor2Tensor project \cite{tensor2tensor} and is trained for 200k steps on a single GPU.

The baseline methods, ODIN and VIB, are dependent on a classification task and learn from the joint distribution of images and labels, while our methods use only images. For the VIB baseline, we use the rate term as the threshold variable. The experiments in \cite{alemi2018uncertainty} make use of (28, 28, 5) ``location-aware'' features concatenated to the model inputs, to assist in distinguishing spatial inhomogeneities in the data. In this work we train vanilla generative models with no special modifications, so we also train VIB without location-aware features. For CIFAR-10 experiments, we train VIB for 26 epochs and converge at 75.7\% classification accuracy on the test set. All other experimental parameters for VIB are identical to those in \cite{alemi2018uncertainty}.

\input{tables/ood_results.tex}

Despite being trained without labels, our methods -- in particular Generative Ensembles -- outperform ODIN and VIB on most OoD tasks. The VAE rate term is robust to adversarial inputs on the IWAE, because the FGSM perturbation primarily minimizes the (larger) distortion component of the variational lower bound. The performance of VAE rate versus VIB also suggests that latent codes learned from generative objectives are more useful for OoD detection that latent codes learned via a classification-specific objective. Surprisingly, despite not being a general estimate of typicality, WAIC dramatically outperforms the $\left\Vert d \right\Vert $ baseline.

We present in Figure~\ref{fig:lowest-highest} images from OoD datasets with the smallest and largest WAICs, respectively. Models trained on FashionMNIST tend to assign higher likelihoods to straight, vertical objects like ``1's'' and pants. It is not altogether surprising that AUROC scores on on the HFlip transformation was low, given that many pants, dresses are symmetric.

\begin{figure}[h]
  \centering
  \includegraphics[width=\linewidth]{figures/failure_fmnist.eps}
  \caption{Left: lowest WAIC for each evaluation dataset. Right: highest WAIC for each evaluation dataset.}
  \label{fig:lowest-highest}
\end{figure}

\subsection{Credit Card Anomaly Detection}

We consider the problem of detecting fraudulent credit card transactions from the Kaggle Credit Fraud Challenge \citep{dal2015calibrating}. A conventional approach to fraud detection is to include a small fraction of fraudulent transactions in the training set, and then learn a discriminative classifier. Instead, we treat fraud detection as an anomaly detection problem where only normal credit card transactions are available at training time. This is motivated by realistic test scenarios, where an adversary is hardly restricted to generating data identically distributed to the training set.

We compare single likelihood models (16-sample IWAE) and Generative Ensembles (ensemble variance of IWAE) to a binary classifier baseline that has access to a training set of fraudulent transactions in Table~\ref{table:creditcard}. The classifier baseline is a fully-connected network with 2 hidden ReLU layers of 512 units, and is trained using a weighted sigmoid cross entropy loss (positive weight=580) with Dropout and RMSProp ($\alpha=1\mathrm{e}{-5}$). The VAE encoder and decoder are fully connected networks with single hidden layers (32 and 30 units, respectively) and trained using Adam ($\alpha=1\mathrm{e}{-3}$).

Unsurprisingly, the classifier baseline performs best because fraudulent test samples are distributed identically to fraudulent training samples. Even so, the single-model density estimation and Generative Ensemble achieve reasonable results.

\subsection{Failure Analysis}

In this section we discuss the experiments in which Generative Ensembles performed poorly, and suggest simple fixes to address these issues.

In our early experiments, we found that a VAE trained on Fashion MNIST performed poorly on all OoD datasets when using $p_\theta({\textnormal{x}})$ and WAIC metrics. This was surprising, since the same metrics performed well when the same VAE architecture was trained on MNIST. To explain this phenomenon, we show in Figure~\ref{fig:fmnist_vae_decode} inputs and VAE-decoded outputs from Fashion MNIST and MNIST test sets. Fashion MNIST images are reconstructed properly, while MNIST images are are barely recognizable after decoding.

\begin{figure*}[t]
  \centering
  \includegraphics[width=\textwidth]{figures/fmnist_true_and_ood.png}
  \caption{Top: Inputs and decoded outputs from a VAE trained on Fashion MNIST($\beta=1$) for Fashion MNIST (left) and MNIST (right). Although Fashion MNIST inputs appear to be better reconstructed (suggesting higher likelihoods), they have comparable distortions to MNIST. The bottom row shows that Fashion MNIST and MNIST test samples have comparable rate-distortion scatter plots and IWAE histograms. This results in poor OoD detection of MNIST on Fashion MNIST images, which is mitigated by training with $\beta=.1$ to encourage better auto-encoding.}
  \label{fig:fmnist_vae_decode}
\end{figure*}

A VAE's training objective can be interpreted as the sum of a pixel-wise autoencoding loss (distortion) and a ``semantic'' loss (rate). Even though Fashion MNIST appears to be better reconstructed in a semantic sense, the distortion values between the FashionMNIST and MNIST test datasets are numerically quite similar, as shown in Figure~\ref{fig:fmnist_vae_decode}. Distortion terms make up the bulk of the IWAE predictions in our models, thus explaining why $p_\theta({\textnormal{x}})$ was not very discriminative when classifying OoD MNIST examples.

\cite{higgins2016beta} propose $\beta$-VAE, a simple modification to the standard VAE objective: $p(x | z) + \beta \cdot D_{\mathrm{KL}} ( q_\theta(z|x) \Vert p(z) )$. $\beta$ controls the relative balance between rate and distortion terms during training. Setting $\beta < 1$ is a commonly prescribed fix for encouraging VAEs to approach the ``autoencoding limit'' and avoid posterior collapse \citep{alemi2018fixing}. At test time, this results in higher-fidelity autoencoding at the expense of higher rates, which seems to be a more useful signal for identifying outliers than the total pixel distortion (also suggested by Table~\ref{table:results_table}, column 4). Re-training the ensemble with $\beta = .1$ encourages a higher distortion penalty during training, and thereby fixes the OoD detection model.

\input{tables/creditcard_ood.tex}

\section*{Acknowledgements}
We thank Manoj Kumar, Peter Liu, Jie Ren, Justin Gilmer, Ben Poole, Augustus Odena, and Balaji Lakshminarayanan for code and valuable discussion. We would also like to thank all the organizers and participants of DL Jeju Camp 2018.

\section{Introduction}
\label{introduction}

Knowing when a machine learning (ML) model is qualified to make predictions on an input is critical to safe deployment of ML technology in the real world. When training and test distributions differ, neural networks may provide -- with high confidence -- arbitrary predictions on inputs that they are unaccustomed to seeing. This is known as the Out-of-Distribution (OoD) problem. In addition to ML safety, identifying OoD inputs (also referred to as anomaly detection) is a crucial feature of many data-driven applications, such as credit card fraud detection and monitoring patient health in medical settings.

A typical OoD scenario is as follows: a machine learning model infers a predictive distribution $p({\textnormal{y}}|{\textnormal{x}})$ from input ${\textnormal{x}}$, which at training time is sampled from a distribution $p({\textnormal{x}})$. At test-time, $p({\textnormal{y}}|{\textnormal{x}})$ is evaluated on a single input sampled from $q(x)$, and the objective of OoD detection is to infer whether $p({\textnormal{x}}) \equiv q({\textnormal{x}})$. This problem may seem ill-posed at first glance, because we wish to compare $p({\textnormal{x}})$ and $q({\textnormal{x}})$ using only a single sample from $q(x)$. Nevertheless, this setup is common when serving ML model predictions over the Internet to anonymous, untrusted users, and is also assumed in adversarial ML literature. Each user generates data from a unique test set $q(x)$ and may only request one prediction.

One approach to OoD detection is to combine a dataset of anomalies with in-distribution data and train a binary classifier to tell them apart, or alternatively, appending a ``None of the above'' category to a classification model. The classifier then learns a decision boundary, or likelihood ratio, between $p({\textnormal{x}})$ and the anomaly distribution $q({\textnormal{x}})$. However, the discriminative approach to anomaly detection requires $q({\textnormal{x}})$ to be specified at training time; this is a severe flaw when anomalous data is rare (e.g. medical seizures) or not known ahead of time, e.g. generated by an adversary.

On the other hand, density estimation techniques do not assume an anomaly distribution at training time, and can be used to assign lower likelihoods to OoD inputs \cite{bishop1994novelty}. However, we present a couple concerns about the appropriateness of likelihood models for OoD detection.

\subsection{Counterintuitive Properties of Likelihood Models}

When $p(x)$ is unknown, a generative model $p_\theta({\textnormal{x}})$, parameterized by $\theta$, can be trained to approximate $p({\textnormal{x}})$ from its samples. Generative modeling algorithms have improved dramatically in recent years, and are capable of learning probabilistic models over massive, high-dimensional datasets such as images, video, and natural language \citep{kingma2018glow, vaswani2017attention, wang2018high}. Autoregressive Models and Normalizing Flows (NF) are fully-observed likelihood models that construct a tractable log-likelihood approximation to the data-generating density $p({\textnormal{x}})$ \citep{uria2016neural, dinh2014nice, rezende2015variational}. Variational Autoencoders (VAE) are latent variable models that maximize a variational lower bound on log density \citep{kingma2013auto, rezende2014stochastic}. Finally, Generative Adversarial Networks (GAN) are implicit density models that minimize a divergence metric between $p({\textnormal{x}})$ and generative distribution $p_\theta({\textnormal{x}})$ \citep{goodfellow2014generative}.

Likelihood models implemented using neural networks are susceptible to malformed inputs that exploit idiosyncratic computation within the model \citep{szegedy2013intriguing}. When judging natural images, we assume an OoD input $x \sim q({\textnormal{x}})$ should remain OoD within some $L^P$-norm, and yet a Fast Gradient Sign Method (FGSM) attack \citep{goodfellow6572explaining} on the predictive distribution can realize extremely high likelihood predictions \citep{nguyen2015deep}. Conversely, a FGSM attack in the reverse direction on an in-distribution sample $x \sim p({\textnormal{x}})$ creates a perceptually identical input with low likelihood \citep{kos2018adversarial}.

An earlier version of this paper, concurrently with work by Nalisnick et al. \yrcite{nalisnick2018dodeep}, and Hendrycks et al. \yrcite{hendrycks2018deep} showed that likelihood models can be fooled by OoD datasets that are not even adversarial by construction. As shown in Figure~\ref{fig:single_model_failure}, a likelihood model trained on the CIFAR-10 dataset yields higher likelihood predictions on SVHN images than the CIFAR-10 training data itself! For a flow-based model with an isotropic Gaussian prior, this implies that SVHN images are systematically projected closer to the origin than the training data \cite{nalisnick2018dodeep}.

\begin{figure}[h]
  \centering
  \includegraphics[width=\linewidth]{figures/cifar-glow-ood.eps}
  \caption{Density estimation models are not robust to OoD inputs. A GLOW model \citep{kingma2018glow} trained on CIFAR-10 assigns much higher likelihoods to samples from SVHN than samples from CIFAR-10. .}
  \label{fig:single_model_failure}
\end{figure}

\subsection{Likelihood and Typicality}
\label{sec:typicality}
Even if we could compute likelihoods exactly, it may not be a sufficient measure for scoring OoD inputs. It is tempting to suggest a simple one-tailed test in which lower likelihoods are OoD, but the intuition that in-distribution inputs should have the highest likelihoods \emph{does not hold in higher dimensions}. For instance, consider an isotropic 784-dimensional Gaussian. A data point at the origin has maximum likelihood, and yet it is highly atypical because the vast majority of probability mass lies in an annulus of radius $\sqrt{784}$. Likelihoods can determine whether a point lies in the support of a distribution, but do not reveal where the probability mass is concentrated.

The main contributions of this work are as follows. First, we observe that likelihood models assign higher densities to OoD datasets than the ones they are trained on (SVHN for CIFAR-10 models, and MNIST for Fashion MNIST models). Second, we propose Generative Ensembles, an anomaly detection algorithm that combines density estimation with uncertainty estimation. Generative Ensembles are trained independently of the task model, and can be implemented using exact or approximate likelihood models. We also demonstrate how predictive uncertainty can be applied to robustify implicit GANs and leverage them for anomaly detection. Finally, we present yet another surprising property of deep generative models that warrants further explanation: density estimation should not be able to account for probability mass, and yet Generative Ensembles outperform OoD baselines on the majority of common OoD detection problems, and demonstrate competitive results with discriminative classification approaches on the Kaggle Credit Fraud dataset.

\section{Generative Ensembles}

We introduce Generative Ensembles, a novel anomaly detection algorithm that combines likelihood models with predictive uncertainty estimation via ensemble variance. Concretely, an ensemble of generative models that compute exact or approximate likelihoods (autoregressive models, flow-based models, VAEs) are used to estimate the Watanabe-Akaike Information Criterion (WAIC).

\subsection{Watanabe Akaike Information Criterion (WAIC)}
First introduced in \citet{watanabe2010asymptotic},
the Watanabe-Akaike Information Criterion (WAIC) gives an
asymptotically correct estimate of the gap between the
training set and test set expectations.
If we had access to samples from the true
Bayesian posterior of a model, we could
compute a corrected version of the expected log
~\citep{watanabegrey}\footnote{
    Note that in this work we form a robust measure of what Watanabe calls the \emph{Gibbs} loss ($\mathbb{E}_{\theta} [ \log p_\theta ({\textnormal{x}}) ]$),
    which while distinct from the \emph{Bayes} loss ($\log \mathbb{E}_{\theta}[ p_\theta({\textnormal{x}})]$) has the same gap asymptotically~\citep{watanabegrey}.
}:
\begin{equation}
\text{WAIC}(x) = \mathbb{E}_\theta[\log p_\theta({\textnormal{x}})] - \mathrm{Var}_\theta\left[\log p_\theta({\textnormal{x}})\right]
\end{equation}
The correction term subtracts the variance in likelihoods
across independent samples from the posterior.
This acts to robustify our estimate, ensuring that points
that are sensitive to the particular choice of posterior parameters
are penalized.

In this work we do not have exact posterior samples,
so we instead utilize independently trained model instances as a
proxy for posterior samples, following
\cite{lakshminarayanan2017simple}.
Being trained with Stochastic Gradient Descent (SGD),
the independent models in the ensemble act as approximate
posterior samples~\citep{mandt2017stochastic}.

\subsection{Does WAIC Address Typicality?}

Although WAIC can protect models against likelihood estimation errors, we show via a toy model that it should not be able to distinguish whether a point is in the typical set. We illustrate this phenomena in Figure~\ref{fig:waic-decreases} on ensembles of Gaussians fitted to samples from an isotropic Gaussian with leave-one-out cross validation. Like likelihoods, the WAIC measure also decreases monotonically from the origin.

\begin{figure}[h]
  \centering
  \includegraphics[width=\linewidth]{figures/waic_decreasing.eps}
  \caption{WAIC estimated using Jackknife resampling of data points drawn from an isotropic Gaussian, for an ensemble of size $N=10$. Lines correspond to a dimensionality of the data.}
  \label{fig:waic-decreases}
\end{figure}

Given that SVHN latent codes lie interior to the CIFAR-10 annulus (Figure~\ref{fig:single_model_failure}), WAIC should fail to distinguish SVHN as OoD. To our surprise, we show in Figure~\ref{fig:waic_fix} and Table~\ref{table:results_table} that not only does WAIC reject SVHN as OoD, but it outperforms a baseline that does test for typicality by measuring the Euclidean distance to the origin!

\begin{figure}[h]
  \centering
  \includegraphics[width=\linewidth]{figures/cifar-glow-waic.eps}
  \caption{We use ensembles of generative models to implement the Watanabe-Akaike Information Criterion (WAIC), which combines density estimation with uncertainty estimation. Histograms correspond to predictions over test sets from each dataset.}
  \label{fig:waic_fix}
\end{figure}

Glow models ought to map the training distribution (CIFAR-10) to a distribution whose probability mass is concentrated in an annulus of radius $\sqrt{3072} (\approx 55.4)$, but as we show in Figure~\ref{fig:radii_interior}, the Glow model actually maps CIFAR, SVHN, and Celeb-A to annuli whose mean radii span the range $42-54$. In our experiments, the variance of radii across models is larger for SVHN and Celeb-A than CIFAR-10, allowing the WAIC metric to correctly identify these as anomalies despite prior hypotheses that WAIC is insufficient.

\begin{figure}[h]
  \centering
  \includegraphics[width=\linewidth]{figures/radii_interior.eps}
  \caption{Histogram of euclidean norms in latent space. Glow models map CIFAR-10, SVHN, and Celeb-A outside of the typical set.}
  \label{fig:radii_interior}
\end{figure}

\subsection{Generative Ensembles of GANs}
\label{sec:gans}

We describe how to improve GAN-based anomaly detection with ensembles. Although we cannot readily estimate WAIC with GANs, we can still leverage the principle of epistemic uncertainty to improve a GAN discriminator's ability to detect OoD inputs.

Figure~\ref{fig:toy2d}b illustrates a simple 2D density modeling task where individual GAN discriminators -- when trained to convergence -- learn a discriminative boundary that does not adequately capture $p({\textnormal{x}})$. Unsurprisingly, a discriminative model tasked with classifying between $p({\textnormal{x}})$ and $q({\textnormal{x}})$ perform poorly when presented with inputs that belong to neither distribution. Despite this apparent shortcoming, GAN discriminators are still applied to successfully to anomaly detection problems \citep{schlegl2017unsupervised, deecke2018anomaly,kliger2018novelty}.

Unlike discriminative anomaly classifiers, which model $p({\textnormal{x}})/q({\textnormal{x}})$ for a static $q({\textnormal{x}})$, the generative distribution $p_\theta({\textnormal{x}})$ of a GAN is trained jointly with the discriminator. The likelihood ratio $p({\textnormal{x}})/p_\theta({\textnormal{x}})$ learned by a GAN discriminator is uniquely randomized by GAN training dynamics on $\theta$ (Figure~\ref{fig:toy2d}b). By training an ensemble of GANs, we can recover an (unnormalized) approximation of $p({\textnormal{x}})$ via decision boundaries between $p({\textnormal{x}})$ and randomly sampled $p_\theta({\textnormal{x}})$ (Figure~\ref{fig:toy2d}c). We implement an anomaly detector using the variance of the discriminator logit, and show that although ensembling GANs lead to far more effective OoD detection than a single GAN (Supplemental Material), it does not outperform our WAIC-based Generative Ensembles.

\begin{figure}[h]
  \centering
  \subfigure[]{\includegraphics[width=.31\linewidth]{figures/NF_heatmap.eps}} \hspace{1pt}
  \subfigure[]{\includegraphics[width=.31\linewidth]{figures/WGAN_heatmap_single_models.eps}} \hspace{1pt}
  \subfigure[]{\includegraphics[width=.31\linewidth]{figures/WGAN_heatmap_ensemble_var.eps}}
  \caption{In this toy example, we learn generative models for a 2D multivariate normal with identity covariance centered at (5, 5). (a) Explicit density models such as Normalizing Flows concentrate probability mass at the data distribution (b) Four independently trained GANs learn random discriminative boundaries, each corresponding to a different implied generator distribution. To ensure that the GAN discriminators form a clear discriminative boundary between $p({\textnormal{x}})$ and $p_\theta({\textnormal{x}})$, we train the discriminators an additional 10k steps to convergence. Each of these boundaries fails to enclose the true data distribution. (c) Predictive uncertainty over an ensemble of discriminators ``fences in'' the shared, low-variance region corresponding to $p({\textnormal{x}})$.}
  \label{fig:toy2d}
\end{figure}

\section{Related Work}
\label{related_work}

Anomaly detection methods are closely intertwined with techniques used in uncertainty estimation, adversarial defense literature, and novelty detection.

\subsection{Uncertainty Estimation}

OoD detection is closely related to the problem of uncertainty estimation, whose goal is to yield calibrated confidence measures for a model's predictive distribution $p({\textnormal{y}}|{\textnormal{x}})$. Well-calibrated uncertainty estimation integrates several forms of uncertainty into $p({\textnormal{y}}|{\textnormal{x}})$: model mispecification uncertainty (OoD detection of invalid inputs), aleatoric uncertainty (irreducible input noise for valid inputs), and epistemic uncertainty (unknown model parameters for valid inputs). In this paper, we study OoD detection in isolation; instead of considering whether $p({\textnormal{y}}|{\textnormal{x}})$ should be trusted for a given $x$, we are trying to determine whether $x$ should be fed into $p({\textnormal{y}}|{\textnormal{x}})$ at all.

Predictive uncertainty estimation is a model-dependent OoD technique because it depends on task-specific information (such as labels and task model architecture) in order to yield an integrated estimate of uncertainty. ODIN \citep{liang2017enhancing}, MC Dropout \citep{gal2016dropout} and DeepEnsemble \citep{lakshminarayanan2017simple} model a calibrated predictive distribution for a classification task. Variational information bottleneck (VIB) \citep{alemi2018uncertainty} performs divergence estimation in latent space to detect OoD, but is technically a model-dependent technique because the latent code is trained jointly with the downstream classification task.

One limitation of model-dependent OoD techniques is that they may discard information about $p({\textnormal{x}})$ in learning the task-specific model $p(y|x)$. Consider a contrived binary classification model on images that learns to solve the task perfectly by discarding all information except the contents of the first pixel (no other information is preserved in the features). Subsequently, the model yields confident predictions on any distribution that happens to preserve identical first-pixel statistics. In contrast, density estimation in data space $x$ considers the structure of the entire input manifold, without bias towards a particular downstream task or task-specific compression.

In our work we estimate predictive uncertainty of the scoring model itself. Unlike predictive uncertainty methods applied to the task model's predictions, Generative Ensembles do not require task-specific labels to train. Furthermore, model-independent OoD detection aids interpretation of predictive uncertainty by isolating the uncertainty component arising from OoD inputs.

\subsection{Adversarial Defense}

Although adversarial attack and defense literature usually considers small $L^p$-norm modifications to input (demonstrating the alarming sensitivity of neural networks), there is no such restriction in practice to the degree with which an input can be perturbed in a test setting. We consider adversarial inputs in the broader context of the OoD problem, where inputs can be swapped with other datasets, transformed and corrupted \cite{hendrycks2018benchmarking}, or are explicitly designed to fool the model.

Song et al. \yrcite{song2017pixeldefend} observe that adversarial examples designed to fool a downstream task tend to have low likelihoods under an independent generative model. They propose a ``data purification'' pipeline, where inputs are modified via gradient ascent on model likelihood before being passing to the classifier. Their evaluations are restricted to $L^p$-norm attacks on in-distribution inputs on the classifier, and do not take into account that the generative model itself may be susceptible to OoD errors. In fact, gradient ascent on model likelihood has the exact opposite of the desired effect when the input is OoD to begin with. In our experiments we measure the degree to which we can identify adversarially perturbed ``rubbish inputs'' as anomalies, and also note that adversarial examples for IWAE predictions have high rates under the latent code, making them suitable for anomaly detection.

\subsection{Novelty Detection}

When learning signals are scarce, such as in reinforcement learning (RL) with sparse rewards, the anomaly detection problem is re-framed as \emph{novelty detection}, whereby an agent attempts to visit states that are OoD with respect to previous experience \citep{fu2017ex2, marsland2003novelty}. Anomaly detection algorithms, including our proposed Generative Ensembles, are directly applicable as novelty bonuses for exploration. However, we point out in Section~\ref{sec:typicality} that likelihoods are deceiving in high dimensions - the point of highest density under a high-dimensional state distribution may be exceedingly rare.

\section{OoD Detection with GAN Discriminators}
\label{appendix:gan}
For MNIST, Fashion MNIST, and CIFAR-10 datasets, we train WGANs and fine tune the discriminators on stationary $p({\textnormal{x}})$ and $p_\theta({\textnormal{x}})$. Table~\ref{table:supplemental_results_table} shows that ensemble variance of the discriminators, $\mathrm{Var}(D)$. Our WGAN model's generator and discriminator share the same architecture with the VAE decoder and encoder, respectively. The discriminator has an additional linear projection layer to its prediction of the Wasserstein metric. We train all WGAN models for 20k generator updates. To ensure $D$ represents a meaningful discriminative boundary between the two distributions, we freeze the generator and fine-tune the discriminator for an additional 4k steps on stationary $p({\textnormal{x}})$ and $p_\theta({\textnormal{x}})$. For CIFAR-10 WGAN experiments, we change the first filter size in the discriminator from 7 to 8.

\input{tables/supplemental_ood_results.tex}
\section{OoD detection with Rate Terms}

Rate is $D_{\mathrm{KL}} ( q_\theta(z|x) \Vert p(z) )$ term in the VAE objective. It represents information loss between encoder distribution and prior over latent code.  Table~\ref{table:supplemental_results_table} shows that rate is a powerful classifier variable in OoD detection. For Fashion MNIST, the same $\beta$-VAE model as the ones for Table 1 of the original paper are used.
\section{Robustness to Perturbations}

\begin{figure}[h]
  \centering
  \includegraphics[width=\linewidth]{figures/downscaledMNIST.png}
  \caption{Sensitivity to perturbations. Left: VAE assigns higher IWAE values to scaled-down MNIST (red) than to original MNIST (blue). Starting from a scale of .4, each scale increment corresponds to an image scale by 2.5\%. The red and blue line represent average IWAE over 1000 scaled and original MNIST samples, resp. They intersect at the point where the scale is 100\%. IWAE plummets on scaled-up MNIST. Right: Red histogram corresponds to IWAE on scaled-down MNIST by a scale of .4 and the blue one on original MNIST}
  \label{fig:single_model_failure}
\end{figure}

\section{WAIC}

A principle problem in modelling is attempting to estimate expectations under the true data distribution, as distinct from expectations estimated from the training set.  \emph{Information criteria} offer a means for estimating the difference~\cite{gelman}. In this work we utilize the \emph{Watanabe Akaike Information Criteria}~\cite{watanabe2010asymptotic}, which gives an asymptotically correct estimate.  For completeness, we give a short derivation here following the style and form of \citet{watanabegreen}.

redpajama_set_name: RedPajamaArXiv


FBN's Susan Li on a report that average 401(k) balances for younger Americans increased 3.3 percent in March, compared to 2.2 for older Americans.
Tax-deductible contributions to 401(k)s, IRAs and other retirement accounts are among the few remaining ways to reduce taxable income if you don't itemize deductions. And few of us do these days: Only about 1 in 10 taxpayers is expected to itemize now that Congress has nearly doubled the standard deduction, tax experts say. That's down from about 1 in 3 before the law changed.
Unreimbursed work expenses, tax prep fees and job search costs are no longer deductible.
Moving expenses aren't deductible unless you're active-duty military.
Casualty and theft losses are deductible only in a federally declared disaster area.
State and local tax deductions are capped at $10,000.
Home equity loan interest is deductible only if the money was used to substantially improve your home.
Student loan interest is still deductible if you don't itemize, as are certain self-employment expenses.
Not everyone can take advantage of those deductions, but the vast majority of working people can contribute to retirement plans, says Michael Eisenberg, a CPA personal finance specialist with the AICPA's National CPA Financial Literacy Commission.
Contribution limits are higher for workplace plans such as 401(k)s: $19,000 for 2019, or $25,000 if you're 50 or older. If you have a workplace plan, your company also likely offers matching funds, says Jarod Taylor, a financial counselor in Westerville, Ohio.
"Let's say you put in 4% (of your pay), and your employer matches that up to 4%," Taylor says. "You just gave yourself a 4% bonus."
Lower-income taxpayers may receive an additional benefit: a tax credit of up to $2,000 for single people or $4,000 for married couples filing jointly that can further reduce the cost of contributions. Tax credits are even more valuable than deductions, and it's rare to get both at the same time.
For many people, tax refunds aren't "extra" money. Every dollar may be needed to catch up on overdue bills or pay for health care.
If that's not the case, though, you could put a chunk of this year's refund into a retirement account. You can do that directly, by opening or contributing to an IRA. Or you can boost your 401(k) contribution and use the refund to help replace money deducted from your paycheck.
If you don't mind a smaller refund and just want to pay less taxes, another possibility is reducing your withholding and channeling the additional money into your retirement fund, Taylor says. Taylor also advises clients to look for small expenses to trim, such as negotiating a cheaper cable package and avoiding bank fees.
"You've already been living without that money, so just funnel that off to saving for retirement," Taylor says.
Even small amounts can add up. As traditional pensions become rarer, it's important for most people to save if they want a comfortable retirement.
"You don't want to work for many, many years and wind up, if you can help it, living on Social Security alone," Eisenberg says.


An extensive geographic footprint across Africa and the Middle East means Vallis can rapidly establish new operations.
Our initial operation in Tunisia dealt with the inspection of vehicles and equipment destined for Dakar, Senegal. Tunisia has become one of the few success stories from the Arab Spring with democratization and a sense of optimism spreading amongst the country's youth.
To complement the expansion of assurance services in Tunisia, we are pleased to present a country report written at our Head Office by Natasha Orr.


No. Kinetis M series are not supported in KDS because are not supported by KSDK.
So KDS and KSDK are released together and KDS supports just devices that are currently supported by KSDK.
do you need work with PE or not?
What are you using for MCU?
If I look at README.txt for 10.6.3 version, I don´t see support for KMx family.
I wonder if Freescale is going to add the KMx family to the PE and if .... when???
PE is an great USP for the Freescale micros!
but either for this case unfortunately is no PEx support.
There will be an update coming out these days supporting KM14, KM33 and KM34 with bare metal projects.
I'll post here a note once it is available for download.
i need to know, the data to download it.


text:
How Beautiful We Were is an environment-themed novel written by Cameroonian–American novelist Imbolo Mbue. It is the second novel written by Mbue. Set in a fictional village in Africa, it follows of a set of villagers who challenge the government and an American oil company.

Background and inspiration
Mbue's novel was influenced by her childhood days in Cameroon. Mbue started writing the novel seventeen years prior to publishing Behold the Dreamers in 2016.
Mbue's "interest was more in writing a story about what happens when a group of people decides to push back against a powerful corporation." She cited Ken Saro-Wiwa and the environmental issues in the Niger Delta as her major influence.

Plot summary
The story takes us to the village of Kosawa—a fictional settlement in Africa under a monarch system of government.
The populace is angered by the entrance of the American oil firm Pexton, which causes an oil spill to the point of causing fatal diseases.
Seeing the sufferings of his people, Kanga—the village mad man—convinces the villager to lead a revolts but is opposed by the president of the country—a man wearing a "leopard skin hat tilted to the right".
The revolution later involves young adults, particularly Thula who is described as a woman of fire.
Thula who had just graduated from a college in the United States forges a militant organisation which she uses to spearhead the social protest against her people.
The company in turn, bribes some of the statesmen with scholarships, transports while the government grants them statutes—sowing a seed of discord in the village.

Characters
 Kanga — described as the village mad man, Kanga convinces Thula to lead a revolt against the government and Pexton.
 Thula Nangi — a young woman who takes over from Kanga in revolting using her knowledge from protest in the United States to dialogue with the government an Pexton.
 Yaya — Thula's grandmother.

Reception
It was listed in Brittle Paper's Notable Books of 2021.

References

2021 novels
Environmental fiction books


text:
Donald Grantham (* 9. November 1947 in Duncan, Oklahoma) ist ein US-amerikanischer Komponist, Dirigent und Hochschullehrer, der hauptsächlich im Bereich der symphonischen Blasmusik tätig ist.

Leben

Donald Grantham studierte von 1967 bis 1970 an der University of Oklahoma und von 1970 bis 1973 an der University of Southern California bei Halsey Stevens, Robert Linn und Ramiro Cortés. 1973 und 1974 studierte er zudem bei Nadia Boulanger in Paris. 1980 schloss er seine Ausbildung mit einem Doctor of Musical Arts an der University of Southern California ab. 1974 begann Grantham, an der University of Southern California zu unterrichten; im Jahr darauf wechselte er an die Butler School of Music der University of Texas at Austin, wo er seit 1991 auch Professor für Komposition ist.

Werke (Auswahl)

Bühnenwerke

 1988: The Boor, komische Oper in einem Akt nach dem Theaterstück "Der Bär" von Anton Tschechow. UA am 24. Februar 1989 am University of Texas Opera Theater.

Blasorchester

 1979: Concerto in One Movement für Bassposaune und Wind Ensemble
 1993: Bum's Rush
 1997: Fantasy Variations
 1998: Southern Harmony
 1999: J'ai été au bal
 2000: Kentucky Harmony
 2001: Don't You See?
 2001: Variations on an American Cavalry Song
 2004–2007: The Barons (Baron Cimetiere's Mambo, Baron Samedi's Sarabande and Soft Shoe, Baron la Croix's Shuffle)
 2005: Court Music
 2007: Starry Crown
 2008: Lone Star Twister
 2009: Symphony for Winds and Percussion

Sinfonieorchester

 1983: El álbum de los duendecitos
 1991: To the Wind's Twelve Quarters
 2000: Exhilaration and Cry
 2004: Fantasy Variations
 2004 Twilight Music

Kammermusik

 1976: Cuatro Caprichos de Francisco Goya für Violine
 1984: The Heart of the Woman nach William Butler Yeats für Sopran und Klavier
 1987: Fantasy on Mr. Hyde's Song für Klarinetten, Flöte, Violine, Violoncello, Perkussion und Klavier
 1990: Life of a Queen nach Lisel Mueller für Sopran, Chor und Harfe
 1990: Lascivious Love Songs nach E. E. Cummings für Mezzosopran und Bläserquintett; auch Mezzosopran und Altsaxophon und Mezzosopran und Klavier
 1990: La noche en la isla nach Pablo Neruda für Bariton, Horn und Klavier
 1993: Slobberin' Goblins für Flöte, Bassklarinette, Horn, Violine, Klavier und Perkussion
 1996: Fantasy Variations on Gershwin's Second Prelude for Piano für zwei Klaviere
 1999: From the Diaries of Adam and Eve nach Mark Twain für Bariton und Klavier oder Sopran, Bariton, Flöte, Violine, Viola, Violoncello und Klavier
 2005: La cancion deseperada nach Pablo Neruda für Sopran, Bass, Violine und Chor

Andere Veröffentlichungen

Auszeichnungen

 1976: Prix Lili Boulanger
 1983: Rudolf Nissim Prize der ASCAP Foundation (El álbum de los duendecitos)
 1990: Guggenheim-Stipendium
 1991: Biennial Composition Prize der National Opera Association (The Boor)
 1993: Erster Platz im Composition Contest der International Horn Society (Slobberin' Goblins)
 1995: William D. Revelli Composition Award der National Band Association (Bum's Rush)
 1998: William D. Revelli Composition Award der National Band Association (Fantasy Variations)
 1999: Sousa/ABA/Ostwald Award der American Bandmasters Association (Fantasy Variations)
 1999: William D. Revelli Composition Award der National Band Association (Southern Harmony)
 2000: Sousa/ABA/Ostwald Award der American Bandmasters Association (Southern Harmony)

Aufnahmen

 Music by Donald Grantham (2001, Centaur CRC-2441), Voices of Change, Jo Boatman (Künstlerische Leitung).
 Composer's Collection: Donald Grantham (2006, GIA Publications 682), North Texas Wind Symphony, Eugene Corporon (Dirigent).
 Southern Harmony: Kabalevsky, Stevens, Grantham, Lauridsen, Copland (2009, Naxos 8572342), Ohio State University Wind Symphony, Russel C. Mikkelson (Dirigent).
 Old Wine in New Bottles: Grantham, Jacob, Bryant, Pann (2012, Naxos 8572762), Youngstown State University Wind Ensemble, Dana Chamber Winds, Stephen L. Gage (Dirigent).

Literatur

Weblinks

 Piquant Press, offizielle Website von Donald Grantham

 Interview mit Donald Grantham, geführt von Bruce Duffie (englisch)

Einzelnachweise

Komponist (Blasmusik)
Komponist (Vereinigte Staaten)
Komponist klassischer Musik (20. Jahrhundert)
Komponist klassischer Musik (21. Jahrhundert)
Person (Oklahoma)
Hochschullehrer (University of Texas at Austin)
US-Amerikaner
Geboren 1947
Mann


The forthcoming summer special CHOTA JADUGAR (3-D) features 38 dogs who are all set to delight the audiences along with child actor Suraj Balajee (of ASOKA fame) and singer/actor S.P. Balasubramaniam. The lead dog, Barkley, provided by Animal Actors Hollywood, was finalized after the director had auditioned 16 dogs in Hollywood provided by three major animal suppliers in Los Angeles. Barkley has worked as a lead dog in several Hollywood productions and is the best trained at Animal Actors. He was able to perform most of the actions in the script without prior training. In this movie, two other look-alike dogs Pixie and Fletcher, who happen to be his siblings were used as his doubles. The look-alike dogs are back-up dogs and were used to speed up production when Barkley was tired. This is a standard practice in U.S. when dogs play lead roles in movies. Besides Barkley, Animal Actors also provided two other dogs, Marcello a poodle with whom Barkley is in love with and Barnum, a boxer who is competing with Barkley for the poodle's attention. The movie also features 38 other dogs that are acting as Barkley's friends in the movie.


text:
Miniature Unguentarium
Miniature Unguentarium -
Imperial (Roman)
ca. 50 - 2nd century
2 3/16 x 9/16 x 9/16 in. (5.6 x 1.4 x 1.4 cm)
Gift of Mary Hamilton Swindler, PhD 1912, Professor of Archaeology
Accession Number: G.45
Classification: Containers and Vessels; Vessels; Unguentaria
Culture/Nationality: Italian (?)
blown glass*, glass*, miniature*, unguentaria*
blown glass - Glassware shaped by blowing air through a blowpipe into a glob of molten glass.
glass - An amorphous, inorganic substance made by fusing silica (silicon dioxide) with a basic oxide; generally transparent but often translucent or opaque. Its characteristic properties are its hardness and rigidity at ordinary temperatures, its capacity for plastic working at elevated temperatures, and its resistance to weathering and to most chemicals except hydrofluoric acid. Used for both utilitarian and decorative purposes, it can be formed into various shapes, colored or decorated. Glass originated as a glaze in Mesopotamia in about 3500 BCE and the first objects made wholly of glass date to about 2500 BCE.
miniature - Use to describe objects and beings of a reduced size or scale compared to the average or normal range for its kind.
unguentaria - Containers probably used to hold ointments and perfume. Early ceramic examples found at Petra (probably 4th-century BCE) were in the typical Hellenistic form of the spindle bottle, but this form was later completely replaced by a series of high-necked types with round to ovoid bodies of varying and apparently standardized forms (from the 1st century BCE onwards). The number of unguentaria found at Petra suggests that they were made locally; their manufacture would have been linked to the myrrh and other unguents that the Nabataeans traded. They have also been found at western sites. Pear-shaped glass unguentaria were later made at various locations in the Arabian peninsula.
G.45_BMC_s_2.jpg
Home Departure and Destination Bryn Mawr College , 10/4/2013 - 12/31/2013
Shifting Sands: Roman Glass in the Bryn Mawr College Collections Bryn Mawr College , 10/15/2007 - 5/30/2008
Mary Hamilton Swindler Donations
<ref name=BMC>cite web |url=http://triarte.brynmawr.edu/objects-1/info/158348 |title=Miniature Unguentarium |author=Bryn Mawr College Library Special Collections |accessdate=1/17/2022 |publisher=Bryn Mawr College</ref>
Your current search criteria is: Object is "Miniature Unguentarium".


BEST GUIDE: Collection In Salesforce – LIST, SET AND MAP.
Collections is a type of variable that can store the multiple numbers of records. For example, List can store multiple number of Account object's records. Let us now have a detailed overview of all collection types.
Lists are the first and most important of the three data types and list can contain any number of records of primitive, collections, sObjects, user-defined and built-in Apex type.
This is one of the most important types of collection and also, it has some system methods which have been tailored specifically to use with List. List index always starts with 0. This is synonymous to the array in Java. A list should be declared with the keyword 'List'.
Below is the list which contains the List of a primitive data type (string), that is the list of cities.
Declaring the initial values of the list is optional. However, we will declare the initial values here. Following is an example which shows the same.
We can declare the nested List as well. It can go up to five levels. This is called the Multidimensional list.
This is the list of set of integers.
The list can contain any number of records, but there is a limitation on heap size to prevent the performance issue and monopolizing the resources.
There are methods available for Lists which we can be utilized while programming to achieve some functionalities like calculating the size of List, adding an element, etc.
Must Read: TwoFactor Authentication In Salesforce / Best Salesforce Security Guide.
A Set is a collection type which contains multiple number of unordered unique records. A Set cannot have duplicate records. Like Lists, Sets can be nested.
We will be defining the set of products which company is selling.
It is a key value pair which contains the unique key for each value. Both key and value can be of any data type.
The following example represents the map of the Product Name with the Product code.
Map values may be unordered and hence we should not rely on the order in which the values are stored and try to access the map always using keys.
Map value can be null. Map keys when declared String is case-sensitive; for example, ABC and abc will be considered as different keys and treated as unique.


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RossTribbianiFleurSexyuniquesoulSsummerTime .TYLERXBOYXDHotPornoBoyKattyCougarCharmingSmileB .RossTribbianiRossTribbianibriannabryantxxxSaraGooldd .FantasyMan619JackSyllarsmallhottsFleurSexy .danielapornoxxSHOWERSHOWrochella21SlickBabes .ELOISBecaSwiftPretty1BrunetteXSEXYeyesTSX .therealonexdirtypancho89YoungWILDcummerMaryStarJanetta .SeanWalk3rSsummerTimeKattyCougarLonelyZara .MrrChristinaTYLERXBOYXDLolaHaDaisyHan .MyMelodieuniquesoulSweetPunishmentDaisyHan .AnnalisaMbisexcouple1therealonexsalvoshow .


text:
Q: Is there any possible memory leak in this code? Josuttis shows an example about a possible allocator use (chapter 15):
void vector<T,Allocator>::reserve(size_type size)
{
    //allocate new memory for size elements
    T* newmem = alloc.allocate (size);
    //copy old elements into new memory
    uninitialized_copy(elems,elems+numElems,newmem);

What happens if uninitialized_copy fails? Is there any memory leak or is ::operator delete magically called (see the comment in the code below):
  template <class InputIter, class ForwIter>
  ForwIter uninitialized_copy(lnputIter beg, InputIter end, ForwIter dest)
  {
    typedef typename iterator_traits<ForwIter>::value_type VT;
    ForwIter save(dest);
    try {
       for (; beg!=end; ++beg,++dest) {
           new (static_cast<void*>(&*dest))VT(*beg);
       }
       return dest;
    }
    catch (...) {
       for (; save!=dest; ++save) {
           save->~VT();
       }
       throw; // will ::operator delete() be called to free the previously allocated memory?
    }
 }

A: As it stands, your code will leak memory, and the uninitialized_copy has to be contained in a try/catch block itself. The whole purpose of rethrowing the exception inside the copy function is so that it is possible to signal atomically that the copy operation failed, so that the ambient code can perform its own cleanup:
T* newmem = alloc.allocate (size);
try { /* copy, reassign pointer, free previous  */ }
catch (...) { alloc.deallocate(newmem); throw; }

A: Run it through valgrind too. If it has leaks, Valgrind will likely find them as well.


This edition of our successful series to support the Cambridge IGCSE Biology syllabus (0610) is fully updated for the revised syllabus for first examination from 2016. Written by an experienced teacher and examiner, Cambridge IGCSE Biology Coursebook with CD-ROM gives comprehensive and accessible coverage of the syllabus content. Suggestions for practical activities are included, designed to help develop the required experimental skills, with full guidance included on the CD-ROM. Study tips throughout the text, exam-style questions at the end of each chapter and a host of revision and practice material on the CD-ROM are designed to help students prepare for their examinations. Answers to the exam-style questions in the Coursebook are provided on the CD-ROM.
Written in accessible language with the international learner in mind.
Activities to help students develop practical and investigate skills.
Key terms are highlighted throughout with definitions found in the glossary.
Students can check their progress through self-assessment questions and exam-style questions at the end of each chapter.