text:
(Reuters) - Russian riot police detained hundreds of Kremlin opponents on Monday, moving swiftly to curb protests over the jailing of activists convicted of attacking police at a rally against President Vladimir Putin.
With the turmoil in Ukraine adding to tension between the Kremlin and its opponents, Putin foes including Alexei Navalny and members of protest band Pussy Riot were hauled away twice by police, first outside a courthouse and later near the Kremlin.
A judge sentenced seven men to prison terms ranging from 2-1/2 to four years after finding them guilty of rioting and violence against police at a protest on May 6, 2012, the day before Putin returned to the presidency for a third term.
The prison sentences and the detention of activists protesting them drew criticism from Europe, adding to strains between Russia and the West at a time when they are deeply at odds over the future of Ukraine.
Outside the court, activists who blame police for clashes at the 2012 anti-Putin rally shouted "shame" and "Maidan" - a reference to the Kiev square at the center of protests that led to the overthrow of Ukrainian President Viktor Yanukovich.
Relatives and lawyers of the seven said they believed the upheaval in Ukraine, where police were among the dead in a conflict the Kremlin blames on opposition leaders and the West, had prompted the court to impose prison sentences as a signal that such actions would not be tolerated in Russia.
"These sentences are cruel and wrong. They were handed down because of the political situation," said defence lawyer Dmitry Agranovsky, who said he would appeal against his client's conviction. "We hope our appeal will show that they made a mistake and the defendants won't have to answer for the Maidan."
A Russian state TV news show host linked the trial with the events in Ukraine in a broadcast on Sunday, saying the bloodshed that killed at least 82 people in Kiev last week had started with actions similar to the 2012 Moscow protest.
Russian authorities say the convicts came to Moscow's Bolotnaya Square bent on rioting and attacked police with improvised weapons such as rocks, bottles and chunks of asphalt.
Kremlin critics say police started the violence in a deliberate attempt to discredit the opposition and quash a wave of protests as Putin, in power since 2000, started a six-year third presidential term. They describe the convicts as victims of a "show trial" designed to suppress dissent.
The British Foreign Office said the sentences highlighted "concerns felt by many about the restrictions on freedom of assembly and expression in Russia".
A spokeswoman for EU foreign policy chief Catherine Ashton, who visited Ukraine on Monday, said it raised questions about the rule of law and "concerns as to the impact of such trials on the exercise of freedom of expression and assembly in Russia".
"The arrest of dozens of people who had gathered outside the court to hear the verdict is an additional source of concern," she added.
Opposition activists said more than 230 people were detained by riot police who grabbed protesters and dragged them to waiting buses. Police put the figure at more than 100.
Pussy Riot's Maria Alyokhina tweeted a photo of her and bandmate Nadezhda Tolokonnikova with the wire-mesh window of a police van as backdrop.
They were released but detained again after dark, when police mounted major presence outside the Kremlin and prevented protesters from gathering, halting people at metal barriers and subway exits. Activists said more than 315 people were detained, while Moscow police put the number at about 70.
The modest crowds indicated that even with a jolt of inspiration from the protests that put Ukraine's Russian-backed president to rout, Putin's opponents would face an uphill battle in reviving the demonstrations that alarmed the Kremlin when they erupted after a disputed parliamentary election in 2011.
Putin remains by far the most popular politician in Russia.
In December, he engineered the release of Tolokonnikova and Alyokhina in an amnesty and freed long-jailed ex-tycoon Mikhail Khodorkovsky with a pardon - moves widely seen as an effort to improve his image before the 2014 Winter Olympics, a prestige project that ended on Sunday in Sochi.
After reading part of the verdict on Friday, trial judge Natalya Nikishina abruptly called a break until Monday, stirring speculation the Kremlin wanted to keep the sentences under wraps until after the Olympic closing ceremony.
One of the eight defendants, a 20-year-old woman, was given a suspended sentence that will allow her to avoid jail.
Putin denies interfering in court cases, but has said anyone who attacks police must be punished. His spokesman, Dmitry Peskov, declined to comment on the verdict or sentences.


text:
Assistant Professor Chris Richards (UK Chemistry) and Professor James Pauly (UK Pharmacy) have been awarded funding to help elucidate the mechanism of nicotine addiction and to identify targets for nicotine cessation therapeutics. The $760,000 grant awarded by the National Institutes of Health is titled "Single Molecule Determination of nAChR Structural Assembly for Therapeutic Targeting."
The consumption of tobacco products is connected to several severe health risks. Smokers have a higher likelihood of developing cardiovascular disease, respiratory disease, and several types of cancer. As a result, approximately 20% of all deaths in the United States are associated with consumption of tobacco products.
"We developed a technique to examine the organelle specific stoichiometry of nicotinic receptors, which is the basis for this grant" said Richards, whose research in single molecule imaging has paved the way to a suite of advanced optical microscopy techniques for studying the assembly, trafficking, and function of membrane receptors involved in nicotine addiction.
This grant will allow the UK researchers to extend their studies to understand how nicotine alters the assembly of membrane receptors in vivo. "We are now using a set of fluorescent protein knock-in mice that contain receptors that have been genetically encoded," Richards said, "which will help us study how nicotinic receptor assembly is altered due to exposure to nicotine."
Richards and Pauly plan to use this approach, which utilizes single molecule imaging of receptors isolated from live animals, to help elucidate the mechanism of nicotine addiction and to identify target nAChR assemblies and phenotypes for nicotine cessation therapeutics.

subset: commoncrawl


text:
If Obamacare is the worst piece of legislation passed by Obama and the Democrats, Dodd-Frank is clearly close behind on the list. This pernicious bill, sponsored by the instigators of the financial and housing crises, will permanently alter our financial markets for the worst just as Obamacare will kill the healthcare sector if it's not repealed. I've met many small business owners who are contemplating a different career because of the crushing burdens from the new financial regulations.
Dodd Frank created the Financial Stability Oversight Council to enshrine "too big to fail" as a permanent government policy. This institution would vitiate the bankruptcy process and allow the federal government to take over any entity that it deems vital to the rest of the economy. Dodd-Frank also vested the Commodity Futures Trading Commission (CFTC) with the power to make infinite and officious regulations that distort the derivatives market. Nobody knows how badly the market will be distorted as a result of these regulations, but we'll probably be debating new programs in ten years from now to fix the problems created by the CFTC.
One of the worst aspects of the Dodd-Frank regime is the creation of the unaccountable Consumer Financial Protection Bureau (CFPB). It enjoys unchecked and unparalleled regulatory powers over consumer financial products and services. These zealous bureaucrats will limit consumer choice and create superfluous and onerous obstacles to obtaining credit. Most egregiously, the Dodd-Frank law gave the CFPB autonomy from the annual appropriations process by housing it within the Federal Reserve.
It's quite peculiar that there hasn't been a wholesale effort to repeal Dodd-Frank or at least significant parts of the law. Nonetheless, House Republicans instituted some minimum limitations in their version of the FY 2013 Financial Services Appropriations bill. The extra funding for CFTC regulations was stripped out and the CFPB would be subjected to the annual appropriations process. Unfortunately, the same cannot be said for their fellow Republicans across the dome.
Yesterday, the Senate Appropriations subcommittee on Financial Services passed the FY 2013 Financial Services Appropriations bill. The bill appropriates $245 million for implementation of Dodd-Frank and beefs up the budget of the CFTC by 50%. It obviously contains no rider to subject the CFPB to the annual appropriations process. Additionally, like all of the appropriations bills passed so far, this one follows the spending levels set by Reid and Obama instead of those established by the House budget. It appropriates more than $1.3 billion more than last year's spending levels. The bill was approved by voice vote.
The full Appropriations Committee will mark up the bill on Thursday. Please call the Republican members of the committee and tell them to stop consummating Obama's most offensive acts into law. Also, tell them to side with Republicans on the issue of overall spending levels in the bill, instead of doing the bidding of Harry Reid and Barack Obama.
Senator Jim DeMint has introduced an amendment to the farm bill that will repeal Dodd-Frank – lock, stock, and barrel. The vote, assuming Harry Reid allows it to occur, will be very illuminating.
Cross-posted from The Madison Project


text:
Process Improvement Tool
Have a question?
We are ready to help.Contact us
Distributor Lookup
Our distributors are positioned to give you outstanding technical support and service.Find a distributor near you
Subscribe to product updates
Newsletters about product innovations, solutions and services, directly in your inbox.
Thank you for subscribing to Omron's eNews. You will shortly receive an e-mail from us. This e-mail contains a link which activates your email newsletter subscription. This method is used due to legal issues and to make certain that no-one can abuse your e-mail address.
We are experiencing technical difficulties. Your form submission has not been successful. Please accept our apologies and try again later. Details: [details]Download
Events and fairs
See our systems in use and consult our experts.View upcoming events


text:
by David Crane defrev at gmail dot com It looks like vehicle armor a.k.a. vehicular armor just got a whole lot cheaper to make. A team at the British Ministry of Defence's (MOD) in-house science and technology center Defence Science and Technology Laboratory (DSTL), the University of Cambridge and QinetiQ have teamed up to develop a process for making a …Read More »
Tag Archives: nighthawk grp
Marvel Custom Tactical 1911 Pistols for Special Operations and Concealed Carry
by David Crane defrev at gmail.com November 30, 2007 There's a custom 1911 pistolsmith out there building tactical guns (1911s) for military Special Operations Forces (SOF) assaulters /operators and civilian CCWs (concealed carry) who hasn't, to this point, received much ink, anywhere. DefenseReview plans to remedy that, right now. We first mentioned him very briefly in our article on the …Read More »
Nighthawk Custom Global Response Pistol (GRP) for Direct Action/CQB
by David Crane [email protected] January 6, 2006 Anyone who knows anything about the custom/semi-custom tactical 1911 pistol field knows how hugely competitive it is out there. That's one of the reasons why the Phoenix-like rise of Nighthawk Custom over approx. the last year and half is so intriguing. Considering the speed and surprise Nighthawk Custom has exhibited in establishing its …Read More »


text:
Source: Board of Governors of the Federal Reserve System (US)
Release: FOMC Press Release
Data for the period prior to 1994 come from the working paper "A New Federal Funds Rate Target Series: September 27, 1982 - December 31, 1993" (Thornton, Federal Reserve Bank of St. Louis, 2005, http://research.stlouisfed.org/wp/2005/2005-032.pdf). Due to an error in the paper values from April 2, 1986 - April 20, 1986 were adjusted manually to 7.3125%. Data from 1994 to the present are derived from FOMC meeting transcripts and FOMC meeting statements, http://www.federalreserve.gov/fomc/.
Effective December 16, 2008, target rate is reported as a range. Current data at https://fred.stlouisfed.org/series/DFEDTARU and https://fred.stlouisfed.org/series/DFEDTARL
Board of Governors of the Federal Reserve System (US), Federal Funds Target Rate (DISCONTINUED) [DFEDTAR], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/DFEDTAR, September 29, 2023.


text:
Capacity Building
One of SkillWorks' key strategies is to build capacity among community-based organizations and workforce partnerships to ensure that they can meet the human resource needs of employers and low and moderate income individuals.
SkillWorks' capacity building component is twofold:
- Using multiple methods, SkillWorks provides technical assistance to funded workforce partnerships, focusing on partnership management and governance, performance management, working with employers, measuring impact, systems change strategies, and other crucial areas that help partnerships meet their stated goals and objectives.
- SkillWorks also seeks to build the capacity of workforce partnerships and workforce system providers to implement and deliver dual-customer workforce services. To this end, SkillWorks will document and disseminate learnings from the initiative, host general trainings and workshop sessions, and create learning networks that include not only our workforce partnerships but also other organizations and grantees.
In Phase II, SkillWorks worked with the Commonwealth Workforce Coalition to deliver and coordinate capacity building services.
Click here to see SkillWorks' Phase III Capacity Building investments.


text:
On Friday, April 29, 2004, Apple Computer will uncage Mac OS X 10.4 "Tiger," providing Mac fans with the best Mac OS X yet. Though I feel that Tiger is a minor upgrade compared to the previous OS X version, Panther, that shouldn't diminish the importance of this release. Apple has been slowly improving its UNIX-based operating system for several years now, and Tiger is the fruit of that labor. Is it enough to make Windows users switch?
That's a tough call. Compared to Windows XP, Mac OS X Tiger is more professional looking and far less likely to be hacked. It rewards computer experience with a lean and mean user interface that doesn't get in your way, as so many Mac aficionados assert. It's rock solid and stable, at least as much as Windows XP with Service Pack 2 (SP2). In short, Tiger is certainly a worthy competitor to XP, despite OS X's diminutive market share.
With Tiger, the areas in which OS X lag behind XP are shrinking. Tiger isn't a great choice for computer novices, because its Spartan UI offers little in the way of XP's hand-holding wizards and task-based folders. It still lacks XP's vast library of readily available software, though many PC-based hardware devices work just fine with Macs. And Macintosh computers--with some exceptions, such as the Mac mini and eMac--still tend to be quite a bit more expensive than comparable PCs.
Where OS X shines, in general, compared to XP is its support for digital media tasks. Grab the $79 iLife '05 suite--a bargain at twice that cost--and you'll find yourself with a set of tools for digital photos, music, home movies, and DVDs that PC users can only dream of. If you care at all about digital media, you should at least be evaluating a Mac, even if you intend to augment your PC and not replace it.
But what about Tiger? For the prosumer--that is, the technically savvy consumer--Tiger will offer a number of enhancements over previous OS X versions. For those who opt to use both Macs and PCs together, Tiger's ability to browse Windows networks has been refined and offers much better performance, for example. (Though I still wish you didn't have to mount Windows shares on your desktop and could just browse them directly from the Finder, as you can using similar features in Windows and Linux.) And the new Spotlight instant search feature delivers on a key Longhorn feature a full year and a half before Microsoft will bestow such a thing upon Windows users. It's speedy and well-done.
Want a safe and secure Web browsing environment for your children? Check out Apple's excellent Safari 2.0 Web browser, which comes free with Tiger. Safari 2.0 isn't susceptible to the frequent attacks IE users suffer from. And it's arguably even safer than Mozilla Firefox, which will likely see itself become the target of more and more hackers as it gains more users. Tiger also supplies much better parental controls than does XP. Unlike XP, you can actually create non-administrator accounts for kids that actually work as advertised. These accounts will prevent your children from accessing--or accidentally deleting--your private data, and can even be configured to ensure they can communicate electronically only with people you trust: If they try to email someone you haven't configured as safe, for example, you'll be prompted to OK the message before it's sent. Tiger's parental controls also extend to other OS X features, like iChat AV instant messaging (IM) and even the system dictionary.
Tiger is also an excellent choice for those with hearing, vision, or other disabilities. A new VoiceOver feature, similar to the Narrator feature in XP, lets you navigate the OS X interface using only a keyboard, accompanied by a robotic voice that explains what's happening onscreen. There is also a large caption option, a zooming feature, and screen flashing for alerts for the hearing impaired.
When Mac OS X was first released four years ago, it was slow and incomplete. But after a series of successive updates, culminating next week with Tiger, OS X is coming of age. You may not be ready to make the switch. But surely, OS X Tiger--perhaps running on a new Mac mini--will be a welcome addition to any technical user's stable of computing tools.


text:
Notable NYC: 3/18–3/24
Saturday 3/18: Lisa Robertson and Uljana Wolf join the Segue Series. Zinc Bar, 4:30 p.m., $5. Sunday 3/19: Michelle Hogmire, Edward Barkin, Claudia Summers, and Matt Basillere celebrate contributions to KGB Bar Lit. KGB Bar, 7 p.m., free. Ariel Francisco, Sally Wen Mao, Jayson P. Smith, Kymani M. Jade, Taylor Lannamann, Glynn Pogue, Halley Furlong-Mitchell, […]...more


text:
'Les Coulisses du Pouvoir'.
Born in Brussels, Jean-Yves Delitte studied to be an architect and designer. This profession remained his daytime job, but in his spare time he found time to build on an oeuvre as a comic artist. He published his first work in Tintin magazine in 1984, mainly short stories scripted by Jean-Luc Vernal. This resulted in his first book in the collection 'Histoires et Légendes', 'Onna', a collection of stories based on Japanese tales (1989).
Also in 1989 he began an association with Philippe Richelle, with whom he has cooperated on several series. The first one was 'Donnington', published in two books by Lombard, and then had a short continuation with Éditions Hélyode in 1995. Delitte and Richelle continued their collaboration with a series of short stories for the Glénat magazines Circus and Vécu. Their short satirical stories for Circus were made under the joint pseudonym Hiko-Noclast. In 1990, Richelle wrote 'Venturi' for Jean-Yves Delitte in Vécu.
'Les Nouveaux Tsars'.
For Casterman, they worked on their political-fiction thriller 'Les Coulisses du Pouvoir' from 1999 until 2004, when Richelle started doing the artwork himself. Delitte returned to Glénat and started writing his own scripts for serials like 'Le Neptune', a series inspired by Jules Verne (since 2003), 'Les Nouveaux Tsars' (since 2004), 'Belem' (since 2006) and 'Black Crow' (2009).
Besides working on his own series, Delitte began collaborations for Glénat Grafica with Xavier Dorison on the one-shot 'Une Aventure des Brigades du Tigre' (2006) and Didier Convard on the steampunk series 'Tanâtos' (since 2007).
'Le Neptune'.


text:
Glossary - Y
Click on letter to jump to the list of terms starting with that letter. Click on a term to jump to the full definition. The excerpts on this page show the first line of each definition.
- YAA — Youth Astronomy Apprenticeships
- YubiKey — A hardware token that can be used in Duo two-factor authentication instead of a phone.


text: For most enterprises it is not enough to make sure their own email platform is secure. If their suppliers are not equally secure, they can be as vulnerable to criminal hackers and data leaks from human error as the weakest link in their supply chain.The combination of a chain of usually small- to medium-size suppliers, the expansion of cloud-based email services and the Bring Your Own Device (BYOD) trend among workers has created what Richard Parris, writing for\u00a0BCW, calls a "complex melting pot of security challenges surrounding the secure transfer of sensitive data via email."\u00a0By now, the advantages and risks of BYOD have been well documented. While it promotes convenience, collaboration and mobile productivity among employees, it is vulnerable to malicious applications, theft and simple carelessness -- employees storing corporate data in public cloud services that are not secure, so they can access it anytime.Companies are increasingly aware of those risks. In May, IBM famously issued a new set of BYOD policies that, among other things, forbid employees to use a competitor's cloud service (no more Dropbox, no more Carbonite, iCloud, etc.), to forward corporate email to private accounts, to transmit unencrypted data, or to use Apple's personal assistant, Siri, due to fears that confidential information might be forwarded to Apple.Jeanette Horan, IBM's chief information officer, told MIT's\u00a0Technology Review that there was, "a tremendous lack of awareness [among employees] as to what constitutes a risk," including forwarding internal corporate emails to webmail inboxes, exposing sensitive company information to possible security breaches.Many companies also require remote wiping capability on employee devices in case they are lost or stolen, plus communication encryption software. They also require employees not to use a single password for multiple sites, and some are forbidding passwords of a single word.But Parris, who formerly held technical and sales management positions at Boeing Computer Services and founded Intercede, argues that securing email also requires identity management -- a system that creates a digital identity for employees and other third parties connected to an enterprise, which will then track, "who is sending which email and information to whom, when and protecting it in transit and at rest."Even that will not ensure protection of the email, he said. "It must also be run on a secure platform that delivers tightly controlled policy to enforce data labeling, digital message signing, encryption and checking of the actual content."Jeff Wilson, principal analyst for security at Infonetics, agrees that an email management platform would help, since "most people are getting email on [multiple] mobile devices that could be lost, stolen, or compromised."But he noted a more basic problem for many companies: "They don't even have an accurate inventory of devices connecting to their network or a framework for building a security policy and buying appropriate security solutions."Those who want to remain in the marketplace may not have a choice about confronting and correcting such vulnerabilities, however. Parris wrote that enterprises that supply high-security customers will have to comply with information security standards set by the Transglobal Secure Collaboration Program (TSCP) for the governments of the UK, the U.S. and NATO.Those standards are backed by enterprises including Lockheed Martin, Thales, Raytheon, Cassidian and General Dynamics for the Signed and Encrypted Email Over The Internet (SEEOTI) initiative.Since email is the primary method of information sharing, enterprises must keep it secure, "to protect intellectual property and to compete in the global business environment," Parris said.


text:
Editor's note: Sylvia Adcock covered aviation safety and security for Newsday from 1996 until 2005. She is editor of NC State magazine, the alumni magazine of North Carolina State University in Raleigh.
(CNN) -- It wasn't long after TWA Flight 800 exploded off the coast of Long Island on a warm July evening in 1996 that the conspiracy theorists emerged. Seventeen years later, they're still here.
I haven't seen a new documentary that purports to repudiate the conclusions of the National Transportation Safety Board, which ruled that the crash of the 747 headed for Paris was caused by a center fuel tank explosion likely ignited by faulty wiring. But I do know that -- despite all the media attention the filmmakers are getting -- their essential claim that a missile hit the plane is not new.
And neither are aircraft fuel tank explosions. In fact, they were considered enough of a danger that the Federal Aviation Administration has put in place new regulations to prevent such events. And the FAA, heavily lobbied by airlines and aircraft manufacturers who want to keep their costs low, does not enact new regulations without reason.
When I covered the four-year investigation into TWA Flight 800 for Newsday, I spent hundreds of hours interviewing investigators, scientists and experts; I walked around inside the center fuel tank of a Boeing 747 (yes, it's that big) at a plant in Seattle; and I continued to cover the aftermath of the NTSB's final report that culminated in an FAA rule to make airlines pump inert gas into fuel tanks to prevent explosions.
I remember being surprised when I first learned of fuel-tank explosions on aircraft, particularly those designed with air-conditioning units below the center tank. At first it seemed unbelievable. But when an aircraft's fuel tank is nearly empty, as is often the case with the center tank, the heat from the air-conditioning packs underneath combined with ambient temperatures can create, well, what seems like a bomb on board.
NTSB: Design flaw doomed TWA 800
In 1990, the fuel tank of a Philippine Airlines plane exploded on the ground in Manila, killing eight people. In 2001, a Thai Airways International plane blew up on the ground in Bangkok when its center tank exploded.
And as recently as 2006, the wing tank of a Boeing 727 exploded on the ground in Bangalore, India.
In 2000, the FAA compiled a list of 26 fuel-tank explosions dating back to 1959.
I also remember the day l learned that the system that tells pilots how much fuel remains includes electrical components located inside the tank. Although aircraft wiring is made to be strong and durable, it is also subject to the effects of aging and abrasion -- so much so that short circuits can occur. On July 17, 1996, the conditions -- a volatile fuel-air mix of vapors in the center fuel tank heated to the point of being explosive, a likely short circuit in fuel-gauge wiring and 230 people on board -- came together with a tragic result.
I can't speak to the motives of the former investigators who are calling for a renewed investigation. But it is important to note that while a poster from the film on epix.com trumpets, "inside investigators finally break their silence," one of the key investigators featured testified about the NTSB's failings before the Senate Judiciary Committee in 1999. It would seem that if he had something more pertinent to say he would have said it long ago.
As for the missile theory, its main proponents were those eyewitnesses who reported seeing a streak of light in the sky on the night of the crash. That streak has been explained as the still-airborne fuselage of the Boeing 747 after the nose of the plane broke off and fell to earth -- and debris recovery patterns confirm this scenario. Metallurgical evidence showed that the fuel tank, the main structural component between the wings, exploded from the inside, with no evidence of a puncture or outside entry.
The filmmakers say they have new radar evidence. I don't know why it hasn't surfaced before now, or how it will contradict the physical evidence that shows the fuel tank exploded from the inside. I do know that when the process turns to fault-finding in aviation accidents, there are many competing interests with a stake in the game -- the pilots' union, aircraft manufacturers and airlines, all with lawyers ready to battle their side.
When I covered aviation safety, it seemed that the NTSB was the only entity with no conflicts of interest. Even the media want a good story -- and what better story than a missile and cover-up? I imagine the same can be said of people who want to sell an idea for a documentary. Fuel-tank explosions and exposed wiring just aren't that sexy.
Conspiracy theories about TWA Flight 800 are a bit like 17-year cicadas. Except they seem to emerge a bit more often.
Follow us on Twitter @CNNOpinion.
Join us on Facebook/CNNOpinion.
|Powered by Social Strata


text:
LOS ANGELES (Reuters) - The animal rights group PETA sued a California restaurant on Wednesday that it says serves outlawed foie gras to patrons, in what appears to be the first lawsuit to enforce a state ban on the delicacy, PETA officials said.
People for the
Ethical Treatment of Animals said that Hot's Kitchen claimed to have
stopped selling foie gras when a ban on producing or selling it took
effect in July, but was offering it as a complimentary side dish to
customers who order "THE Burger."
Read more in the Chicago Tribune...
Thursday, November 29, 2012
Subscribe to: Posts (Atom)


text:
A beautiful little gift set for a baby shower or new little babe.
Vintage hand knit cardigan with matching bonnet and booties.
beautifully knit with a drawstring around collar.
Matching bonnet and booties.
Excellent condition
Estimate size 3/6M
Cardigan measures:
8" across chest
9" long
Just contact me within: 3 days of delivery
Ship items back to me within: 7 days of delivery
But please contact me if you have any problems with your order.
Because of the nature of these items, unless they arrive damaged or defective, I can't accept returns for:
All sizes are listed in the item's title, at the very end. If you need the measurements, please check the item details section, right below the item overview. You will need to click the arrow to open that tab if you are on your mobile device.
No, this item is vintage and therefore only one is available. Sometimes I may come across two of the same item and may have that one listed as well.
Yes. I will ship internationally via First Class International mail. Customers are responsible for any custom/VAT fees. This may be additional fees, not included in the price of the items. We will not alter customs forms in any way to change price of items, or mark them as gifts. This would be mail fraud.
No, all sales are final. We list all measurements in the item details section. If you are still not sure the item will fit and need more details, please contact us.
We try and get orders shipped out within 2 business days. Sometimes, we even ship on the same day! But, if you need your order right away, we suggest you send us an email first to make sure that we can ship in time. Want it super fast? You have the option of choosing Priority mail shipping at checkout.
We now offer custom gift wrapping! We have various vintage gift wrap available to match your vintage one of a kind gift! We will do our best to pair it with the appropriate theme, i.e. girl, boy, unisex, holiday, or anniversary, but if you have a preference, please feel free to contact us once you've checked out.


text:
Ground Vibration Testing
The ground vibration test (GVT) system is necessary to assure the aeroelastic and aeroservoelastic stability of new and modified aircraft. These GVT data are used to validate analytical vibration and flight control models by measuring flight control transfer functions and structural frequency response functions. The GVT supports flight test activities by assisting in flutter test planning and resolving in-flight aeroelastic related anomalies. It is also used to detect structural flaws and troubleshoot flight control dynamic problems. The GVT system consists of state-of-the-art equipment including aircraft soft-support, structural excitation, vibration sensors, and data acquisition and analysis tools.
Soft-support systems are used to simulate the free-free (unconstrained) boundary conditions that an aircraft experiences during flight. Three soft-support systems are available to test aircraft ranging from small remotely piloted vehicles to aircraft weighing up to 45 metric tons (50 english tons). The F-18 High Alpha Research Vehicle (HARV) is shown mounted on a soft-support system.
Structural excitation is realized using up to 14 electrodynamic shakers. Four shakers provide peak forces of 670 N (150 lb), 8 provide peak forces of 220 N (50 lb), and 2 smaller shakers provide 44 N (10 lb) of force. Programmable signal generators provide a variety of random and deterministic waveforms.
Accelerometers are used to measure the response of the structure. More than 300 accelerometers are available to measure the response of a test vehicle adequately. High-temperature and noncontacting measurements can be done using laser vibrometers.
The computer-based data acquisition and analysis system is the heart of the GVT capability. It can acquire 200 channels of response data simultaneously and provides a large variety of data processing functions including parameter identification, spectral analysis, and test/analysis correlation. The system can generate finite element models and perform vibration dynamics analyses. It also has a sophisticated graphics display capability for visualizing static and animated information. The GVT system is networked to support correlation of analyses with other engineering disciplines.


text:
Eclipse 3.1
PTP 1.0 was released in March 2006. This is the original release plan.
- PTP 1.0 Plan
- Eclipse.org Release Review for 1.0 was held on 15 Aug 2006, and we passed with flying colors.
PTP 1.1 was released in March 2007. This is the original release plan. The release review for 1.1 was held on March 9, 2007.
- PTP 1.1 Plan
- Slides used for the review can be found here.
PTP 2.0 will be be released in March 2008. This release will include some significant new functionality.
- PTP 2.0 Plan
- Slides showing a preview of what will be included in the 2.0 release are available here: PDF_2.0.pdf
The PTP 2.1 release is scheduled for October 31, 2008. This release will update PTP to support Ganymede (including CDT 5.0) as well as provide a new resource manager framework, updates to the debugger and the PLDT, and support for remote development.
The PTP 3.0 release is scheduled for November 30, 2009. This release will update PTP to support Galileo (Eclipse 3.5, CDT 6.0, and RSE 3.1) as well as provide improved resource manager support, updates to the debugger and the PLDT, and improved support for remote development.
The Photran 5.0 release is scheduled to coincide with the PTP 3.0. release. This will be the first official release of Photran.
PTP 4.0 and Photran 6.0 will be part of the Eclipse Helios (3.6) Release in June 2010.
PTP 5.0 and Photran 7.0 will be part of the Eclipse Indigo (3.7) Release in June 2011
PTP 6.0 and Photran 8.0 will be part of the Eclipse Juno (4.2) Release in June 2012
PTP 7.0 and Photran 8.1 will be part of the Eclipse Kepler (4.3) Release in June 2013


text:
In just a few short months, Kris Allen will have a mini-me on his hands.
The "American Idol" alum, who announced his wife's pregnancy in January after getting into a bad car accident, revealed that the couple is expecting a baby boy.
Details from Kris Allen's scary head-on collision earlier this year
"I'm very excited," Allen told Hollywire.com. "I've always wanted to be a dad...so, it's coming in July."
"My wife has already started buying him some clothes like me...he does have some fedoras," he added.
Kris and Katy, who were high school sweethearts, tied the knot in September 2008.
Take a look at Kate Middleton's royal baby bump pics!
U.S. & World
News from around the country and around the globe
Kris Allen's wife isn't the only star rocking a baby bump this spring.


text:
Scientists are reporting the development of a new, ultra-light form of "frozen smoke" — renowned as the world's lightest solid material — with amazing strength and an incredibly large surface area. The new so-called "multiwalled carbon nanotube (MCNT) aerogel" could be used in sensors to detect pollutants and toxic substances, chemical reactors, and electronics components. A report about the material appears in ACS Nano, a monthly journal.
Lei Zhai and colleagues explain that aerogels made from silicon dioxide (the main ingredient in sand) and other material already are used as thermal insulation in windows and buildings, tennis rackets, sponges to clean up oil spills, and other products. Aerogels are solid but so light that they have been compared to frozen smoke. However, only a few scientists have succeeded in making aerogels from carbon nanotubes, wisps of carbon so small that almost 50,000 would fit across the width of a human hair.
The report describes a process for making MCNT aerogels and tests to determine their properties. MCNT aerogels infused with a plastic material are flexible, for instance, like a spring that can be stretched thousands of times. If the nanotubes in a one-ounce cube were unraveled and placed side-to-side and end-to-end, they would carpet three football fields. The MCNT aerogels also are excellent conductors of electricity, making them ideal for sensing applications, such as sensing as little as 0.003527 ounce of a material resting in the palm of one hand, the report indicates.
ARTICLE FOR IMMEDIATE RELEASE "Ultralight Multiwalled Carbon Nanotube Aerogel"
DOWNLOAD FULL TEXT ARTICLE http://pubs.acs.org/stoken/presspac/presspac/full/10.1021/nn102246a
Lei Zhai, Ph.D.
University of Central Florida
Chemistry Department
Phone: 407-882-2847
E-mail: firstname.lastname@example.com
ACS Nano


text:
DENVER -- The Colorado Rockies and star first baseman Todd Helton are facing the harsh reality on the eve of his farewell season that the longtime face of the franchise now has a police mug shot.
The five-time All-Star was arrested about 2:30 a.m. Wednesday in the Denver suburb of Thornton and charged with drunken driving and careless driving.
In a statement released by the Rockies, Helton said he's embarrassed and apologized to his family, the team and the community.
"Last evening I exercised poor judgment and was charged with drinking and driving after driving to a gas station near my house," Helton said in a statement released through the team. "I am very sorry and embarrassed by my actions. I hold myself to a high standard and take my responsibility as a public figure very seriously. My entire career I have worked to set a positive example for my family and in our community and I fell far short of this standard.
"I sincerely ask my family, the Colorado Rockies organization, Major League Baseball and the community to accept my apology. I make no excuses and accept full responsibility for my actions. I humbly ask your forgiveness."
Helton was arrested after someone called police to report a drunken driver, police said. They say Helton's blood-alcohol content was over the legal limit but they haven't released the number.
Helton is a career .320 hitter. The 39-year-old played only 69 games last year before undergoing hip surgery in August. Afterward, the smooth-fielding first baseman known as much for his glove work as for his big bat considered retiring but decided last month to return for the final season of his contract.
"We were extremely disappointed to learn that Todd was arrested this morning. This type of behavior is taken very seriously by our organization," the Rockies said in a statement. "We know that he clearly understands the seriousness of his poor decision, the harm that could have been inflicted on others and the embarrassment his mistake has caused to himself, his family, the Colorado Rockies organization and to Major League Baseball.
"Todd is taking full accountability for his actions with his family, his fans and the organization. The man we have grown to know has strong values that are grounded in his family and hard work. Todd clearly understands the severity of the situation."
Helton is the most accomplished player in Rockies history, collecting 2,420 hits, 354 home runs and 1,345 RBIs over his career.
He debuted in 1997 and took over the following season as Colorado's full-time first baseman following Andres Galarraga's departure to Atlanta as a free agent.
He and Denver Broncos quarterback Peyton Manning are good friends after playing football together at the University of Tennessee in the 1990s. They recently went on a hunting trip after the Broncos' unexpected early exit from the NFL playoffs.
Manning has credited Helton and the Rockies with helping him recover from a series of neck operations that led to his departure from Indianapolis by allowing him to work out at Coors Field during the NFL lockout two summers ago.
Helton, who lives in Thornton year-round, was driving a Ford F-150 pickup at the time of his arrest early Wednesday, police said. He was taken to the police department where he was booked, issued a summons and released to a friend, Thornton police spokesman Matt Barnes said.
The Rockies, under new manager Walt Weiss, begin reporting to spring training this weekend as they try to bounce back from a 64-98 season.


text:
casting for disney channel – Shake it Up season 3 may be coming soon in 2012 and maybe you too will have a shot to audition for 'Shake It Up'.
Shake It Up premiered on November 7, 2010 and the Disney Channel show was created by Chris Thompson. Shake It Up stars Bella Thorne as CeCe Jones and Zendaya as Rocky Blue.
When the show was cast over 200 girls auditioned for the parts of CeCe and Rocky – Bella Thorne and Zendaya were the lucky and talented girls who got the part. Bella Thorne had never been a dancer and began taking dance classes and Zendaya had a background in theater. Casting directors for Disney later commented that the 2 girls were chosen because they had great energy.
The two friends become background dancers on a show called Shake It Up that is in Chicago. The series is their adventures both on and off the Shake It Up set as they try to keep their social lives, school lives and new found stardom all in balance.
The 2 girls are best friends and end up in many comedic situations. The show within a show format is one that Disney Channel used before with shows like Sonny with a Chance and The Famous Jett Jackson. The show within a show format creates a TV series that is about another series or people who act in another series. Shake It Up takes a dance twist because the two girls are dancers. The show is kinda like Hannah Montana, except with dance instead of singing.
Shake It Up also stars Davis Cleveland, Roshon Fegan, Adam Irigoyen, Kenton Duty and Caroline Sunshine.
Shake it Up cast:
- Bella Thorne as CeCe Jones
- Zendaya as Rocky Blue
- Davis Cleveland as Flynn Jones
- Roshon Fegan as Ty Blue
- Adam Irigoyen as Deuce Martinez
- Kenton Duty as Gunther Hessenheffer
- Caroline Sunshine as Tinka Hessenheffer
Besides the main cast above there are actors who are recurring yet don't appear in every single episode.
Season 2 is airing now and hopefully season 3 will start taping very soon if the show gets renewed by Disney Channel.
check out this Shake it Up video:
Every episode of the show uses extras and other actors to support the main cast. Supporting actors and extras are cast through casting agents and those casting calls go out as needed when the show is


text:
Computing Your Skill
Summary: I describe how the TrueSkill algorithm works using concepts you're already familiar with. TrueSkill is used on Xbox Live to rank and match players and it serves as a great way to understand how statistical machine learning is actually applied today. I've also created an open source project where I implemented TrueSkill three different times in increasing complexity and capability. In addition, I've created a detailed supplemental math paper that works out equations that I gloss over here. Feel free to jump to sections that look interesting and ignore ones that seem boring. Don't worry if this post seems a bit long, there are lots of pictures.
It seemed easy enough: I wanted to create a database to track the skill levels of my coworkers in chess and foosball. I already knew that I wasn't very good at foosball and would bring down better players. I was curious if an algorithm could do a better job at creating well-balanced matches. I also wanted to see if I was improving at chess. I knew I needed to have an easy way to collect results from everyone and then use an algorithm that would keep getting better with more data. I was looking for a way to compress all that data and distill it down to some simple knowledge of how skilled people are. Based on some previous things that I had heard about, this seemed like a good fit for "machine learning."
But, there's a problem.
Machine learning is a hot area in Computer Science— but it's intimidating. Like most subjects, there's a lot to learn to be an expert in the field. I didn't need to go very deep; I just needed to understand enough to solve my problem. I found a link to the paper describing the TrueSkill algorithm and I read it several times, but it didn't make sense. It was only 8 pages long, but it seemed beyond my capability to understand. I felt dumb. Even so, I was too stubborn to give up. Jamie Zawinski said it well:
"Not knowing something doesn't mean you're dumb— it just means you don't know it."
I learned that the problem isn't the difficulty of the ideas themselves, but rather that the ideas make too big of a jump from the math that we typically learn in school. This is sad because underneath the apparent complexity lies some beautiful concepts. In hindsight, the algorithm seems relatively simple, but it took me several months to arrive at that conclusion. My hope is that I can short-circuit the haphazard and slow process I went through and take you directly to the beauty of understanding what's inside the gem that is the TrueSkill algorithm.
Skill ≈ Probability of Winning
Skill is tricky to measure. Being good at something takes deliberate practice and sometimes a bit of luck. How do you measure that in a person? You could just ask someone if they're skilled, but this would only give a rough approximation since people tend to be overconfident in their ability. Perhaps a better question is "what would the units of skill be?" For something like the 100 meter dash, you could just average the number of seconds of several recent sprints. However, for a game like chess, it's harder because all that's really important is if you win, lose, or draw.
It might make sense to just tally the total number of wins and losses, but this wouldn't be fair to people that played a lot (or a little). Slightly better is to record the percent of games that you win. However, this wouldn't be fair to people that beat up on far worse players or players who got decimated but maybe learned a thing or two. The goal of most games is to win, but if you win too much, then you're probably not challenging yourself. Ideally, if all players won about half of their games, we'd say things are balanced. In this ideal scenario, everyone would have a near 50% win ratio, making it impossible to compare using that metric.
Finding universal units of skill is too hard, so we'll just give up and not use any units. The only thing we really care about is roughly who's better than whom and by how much. One way of doing this is coming up with a scale where each person has a unit-less number expressing their rating that you could use for comparison. If a player has a skill rating much higher than someone else, we'd expect them to win if they played each other.
The key idea is that a single skill number is meaningless. What's important is how that number compares with others. This is an important point worth repeating: skill only makes sense if it's relative to something else. We'd like to come up with a system that gives us numbers that are useful for comparing a person's skill. In particular, we'd like to have a skill rating system that we could use to predict the probability of winning, losing, or drawing in matches based on a numerical rating.
We'll spend the rest of our time coming up with a system to calculate and update these skill numbers with the assumption that they can be used to determine the probability of an outcome.
What Exactly is Probability Anyway?
You can learn about probability if you're willing to flip a coin— a lot. You flip a few times:
Heads, heads, tails!
Each flip has a seemingly random outcome. However, "random" usually means that you haven't looked long enough to see a pattern emerge. If we take the total number of heads and divide it by the total number of flips, we see a very definite pattern emerge:
But you knew that it was going to be a 50-50 chance in the long run. When saying something is random, we often mean it's bounded within some range.
It turns out that a better metaphor is to think of a bullseye that archers shoot at. Each arrow will land somewhere near that center. It would be extraordinary to see an arrow hit the bullseye exactly. Most of the arrows will seem to be randomly scattered around it. Although "random," it's far more likely that arrows will be near the target than, for example, way out in the woods (well, except if I was the archer).
This isn't a new metaphor; the Greek word στόχος (stochos) refers to a stick set up to aim at. It's where statisticians get the word stochastic: a fancy, but slightly more correct word than random. The distribution of arrows brings up another key point:
All things are possible, but not all things are probable.
Probability has changed how ordinary people think, a feat that rarely happens in mathematics. The very idea that you could understand anything about future outcomes is such a big leap in thought that it baffled Blaise Pascal, one of the best mathematicians in history.
In the summer of 1654, Pascal exchanged a series of letters with Pierre de Fermat, another brilliant mathematician, concerning an "unfinished game." Pascal wanted to know how to divide money among gamblers if they have to leave before the game is finished. Splitting the money fairly required some notion of the probability of outcomes if the game would have been played until the end. This problem gave birth to the field of probability and laid the foundation for lots of fun things like life insurance, casino games, and scary financial derivatives.
But probability is more general than predicting the future— it's a measure of your ignorance of something. It doesn't matter if the event is set to happen in the future or if it happened months ago. All that matters is that you lack knowledge in something. Just because we lack knowledge doesn't mean we can't do anything useful, but we'll have to do a lot more coin flips to see it.
Aggregating Observations
The real magic happens when we aggregate a lot of observations. What would happen if you flipped a coin 1000 times and counted the number of heads? Lots of things are possible, but in my case I got 505 heads. That's about half, so it's not surprising. I can graph this by creating a bar chart and put all the possible outcomes (getting 0 to 1000 heads) on the bottom and the total number of times that I got that particular count of heads on the vertical axis. For 1 outcome of 505 total heads it would look like this:
Not too exciting. But what if we did it again? This time I got 518 heads. I can add that to the chart:
Doing it 8 more times gave me 489, 515, 468, 508, 492, 475, 511, and once again, I got 505. The chart now looks like this:
And after a billion times, a total of one trillion flips, I got this:
In all the flips, I never got less than 407 total heads and I never got more than 600. Just for fun, we can zoom in on this region:
As we do more sets of flips, the jagged edges smooth out to give us the famous "bell curve" that you've probably seen before. Math guys love to refer to it as a "Gaussian" curve because it was used by the German mathematician Carl Gauss in 1809 to investigate errors in astronomical data. He came up with an exact formula of what to expect if we flipped a coin an infinite number of times (so that we don't have to). This is such a famous result that you can see the curve and its equation if you look closely at the middle of an old 10 Deutsche Mark banknote bearing Gauss's face:
Don't miss the forest from all the flippin' trees. The curve is showing you the density of all possible outcomes. By density, I mean how tall the curve gets at a certain point. For example, in counting the total number of heads out of 1000 flips, I expected that 500 total heads would be the most popular outcome and indeed it was. I saw 25,224,637 out of a billion sets that had exactly 500 heads. This works out to about 2.52% of all outcomes. In contrast, if we look at the bucket for 450 total heads, I only saw this happen 168,941 times, or roughly 0.016% of the time. This confirms your observation that the curve is denser, that is, taller at the mean of 500 than further away at 450.
This confirms the key point: all things are possible, but outcomes are not all equally probable. There are longshots. Professional athletes panic or 'choke'. The world's best chess players have bad days. Additionally, tales about underdogs make us smile— the longer the odds the better. Unexpected outcomes happen, but there's still a lot of predictability out there.
It's not just coin flips. The bell curve shows up in lots of places like casino games, to the thickness of tree bark, to the measurements of a person's IQ. Lots of people have looked at the world and have come up with Gaussian models. It's easy to think of the world as one big, bell shaped playground.
But the real world isn't always Gaussian. History books are full of "Black Swan" events. Stock market crashes and the invention of the computer are statistical outliers that Gaussian models tend not to predict well, but these events shock the world and forever change it. This type of reality isn't covered by the bell curve, what Black Swan author Nassim Teleb calls the "Great Intellectual Fraud." These events would have such low probability that no one would predict them actually happening. There's a different view of randomness that is a fascinating playground of Benoît Mandelbrot and his fractals that better explain some of these events, but we will ignore all of this to keep things simple. We'll acknowledge that the Gaussian view of the world isn't always right, no more than a map of the world is the actual terrain.
The Gaussian worldview assumes everything will typically be some average value and then treats everything else as increasingly less likely "errors" as you exponentially drift away from the center (Gauss used the curve to measure errors in astronomical data after all). However, it's not fair to treat real observations from the world as "errors" any more than it is to say that a person is an "error" from the "average human" that is half male and half female. Some of these same problems can come up treating a person as having skill that is Gaussian. Disclaimers aside, we'll go along with George Box's view that "all models are wrong, but some models are useful."
Gaussian Basics
Gaussian curves are completely described by two values:
- The mean (average) value which is often represented by the Greek letter μ (mu)
- The standard deviation, represented by the Greek letter σ (sigma). This indicates how far apart the data is spread out.
In counting the total number heads in 1000 flips, the mean was 500 and the standard deviation was about 16. In general, 68% of the outcomes will be within ± 1 standard deviation (e.g. 484-516 in the experiment), 95% within 2 standard deviations (e.g. 468-532) and 99.7% within 3 standard deviations (452-548):
An important takeaway is that the bell curve allows for all possibilities, but each possibility is most definitely not equally likely. The bell curve gives us a model to calculate how likely something should be given an average value and a spread. Notice how outcomes sharply become less probable as we drift further away from the mean value.
While we're looking at the Gaussian curve, it's important to look at -3σ away from the mean on the left side. As you can see, most of the area under the curve is to the right of this point. I mention this because the TrueSkill algorithm uses the -3σ mark as a (very) conservative estimate for your skill. You're probably better than this conservative estimate, but you're most likely not worse than this value. Therefore, it's a stable number for comparing yourself to others and is useful for use in sorting a leaderboard.
3D Bell Curves: Multivariate Gaussians
A non-intuitive observation is that Gaussian distributions can occur in more than the two dimensions that we've seen so far. You can sort of think of a Gaussian in three dimensions as a mountain. Here's an example:
In this plot, taller regions represent higher probabilities. As you can see, not all things are equally probable. The most probable value is the mean value that is right in the middle and then things sharply decline away from it.
In maps of real mountains, you often see a 2D contour plot where each line represents a different elevation (e.g. every 100 feet):
The closer the lines on the map, the sharper the inclines. You can do something similar for 2D representations of 3D Gaussians. In textbooks, you often just see 2D representation that looks like this:
This is called an "isoprobability contour" plot. It's just a fancy way of saying "things that have the same probability will be the same color." Note that it's still in three dimensions. In this case, the third dimension is color intensity instead of the height you saw on a surface plot earlier. I like to think of contour plots as treasure maps for playing the "you're getting warmer…" game. In this case, black means "you're cold," red means "you're getting warmer…," and yellow means "you're on fire!" which corresponds to the highest probability.
See? Now you understand Gaussians and know that "multivariate Gaussians" aren't as scary as they sound.
Let's Talk About Chess
There's still more to learn, but we'll pick up what we need along the way. We already have enough tools to do something useful. To warm up, let's talk about chess because ratings are well-defined there.
In chess, a bright beginner is expected to have a rating around 1000. Keep in mind that ratings have no units; it's just a number that is only meaningful when compared to someone else's number. By tradition, a difference of 200 indicates the better ranked player is expected to win 75% of the time. Again, nothing is special about the number 200, it was just chosen to be the difference needed to get a 75% win ratio and effectively defines a "class" of player.
I've slowly been practicing and have a rating around 1200. This means that if I play a bright beginner with a rating of 1000, I'm expected to win three out of four games.
We can start to visualize a match between me and bright beginner by drawing two bell curves that have a mean of 1000 and 1200 respectively with both having a standard deviation of 200:
The above graph shows what the ratings represent: they're an indicator of how we're expected to perform if we play a game. The most likely performance is exactly what the rating is (the mean value). One non-obvious point is that you can subtract two bell curves and get another bell curve. The new center is the difference of the means and the resulting curve is a bit wider than the previous curves. By taking my skill curve (red) and subtracting the beginner's curve (blue), you'll get this resulting curve (purple):
Note that it's centered at 1200 - 1000 = 200. Although interesting to look on its own, it gives some useful information. This curve is representing all possible game outcomes between me and the beginner. The middle shows that I'm expected to be 200 points better. The far left side shows that there is a tiny chance that the beginner has a game where he plays as if he's 700 points better than I am. The far right shows that there is a tiny chance that I'll play as if I'm 1100 points better. The curve actually goes on forever in both ways, but the expected probability for those outcomes is so small that it's effectively zero.
As a player, you really only care about one very specific point on this curve: zero. Since I have a higher rating, I'm interested in all possible outcomes where the difference is positive. These are the outcomes where I'm expected to outperform the beginner. On the other hand, the beginner is keeping his eye on everything to the left of zero. These are the outcomes where the performance difference is negative, implying that he outperforms me.
We can plug a few numbers into a calculator and see that there is about a 24% probability that the performance difference will be negative, implying the beginner wins, and a 76% chance that the difference will be positive, meaning that I win. This is roughly the 75% that we were expecting for a 200 point difference.
This has been a bit too concrete for my particular match with a beginner. We can generalize it by creating another curve where the horizontal axis represents the difference in player ratings and the vertical axis represents the total probability of winning given that rating difference:
As expected, having two players with equal ratings, and thus a rating difference of 0, implies the odds of winning are 50%. Likewise, if you look at the -200 mark, you see the curve is at the 24% that we calculated earlier. Similarly, +200 is at the 76% mark. This also shows that outcomes on the far left side are quite unlikely. For example, the odds of me winning a game against Magnus Carlsen, who is at the top of the chess leaderboard with a rating of 2813, would be at the -1613 mark (1200 - 2813) on this chart and have a probability near one in a billion. I won't hold my breath. (Actually, most chess groups use a slightly different curve, but the ideas are the same. See the accompanying math paper for details.)
All of these curves were probabilities of what might happen, not what actually happened. In actuality, let's say I lost the game by some silly blunder (oops!). The question that the beginner wants to know is how much his rating will go up. It also makes sense that my rating will go down as a punishment for the loss. The harder question is just how much should the ratings change?
By winning, the beginner demonstrated that he was probably better than the 25% winning probability we thought he would have. One way of updating ratings is to imagine that each player bets a certain amount of his rating on each game. The amount of the bet is determined by the probability of the outcome. In addition, we decide how dramatic the ratings change should be for an individual game. If you believe the most recent game should count 100%, then you'd expect my rating to go down a lot and his to go up a lot. The decision of how much the most recent game should count leads to what chess guys call the multiplicative "K-factor."
The K-Factor is what we multiply a probability by to get the total amount of a rating change. It reflects the maximum possible change in a person's rating. A reasonable choice of a weight is that the most recent game counts about 7% which leads to a K-factor of 24. New players tend to have more fluctuations than well-established players, so new players might get a K-Factor of 32 while grand masters have a K-factor around
10. Here's how the K-Factor changes with respect to how much the latest game should count:
Using a K-Factor of 24 means that my rating will now be lowered to 1182 and the beginner's will rise to 1018. Our curves are now closer together:
Note that our standard deviations never change. Here are the probabilities if we were to play again:
This method is known as the Elo rating system, named after Arpad Elo, the chess enthusiast who created it. It's relatively simple to implement and most games that calculate skill end here.
I Thought You Said You'd Talk About TrueSkill?
Everything so far has just been prerequisites to the main event; the TrueSkill paper assumes you're already familiar with it. It was all sort of new to me, so it took awhile to get comfortable with the Elo ideas. Although the Elo model will get you far, there are a few notable things it doesn't handle well:
- Newbies - In the Elo system, you're typically assigned a "provisional" rating for the first 20 games. These games tend to have a higher K-factor associated with them in order to let the algorithm determine your skill faster before it's slowed down by a non-provisional (and smaller) K-factor. We would like an algorithm that converges quickly onto a player's true skill (get it?) to not waste their time having unbalanced matches. This means the algorithm should start giving reasonable approximations of skill within 5-10 games.
- Teams - Elo was explicitly designed for two players. Efforts to adapt it to work for multiple people on multiple teams have primarily been unsophisticated hacks. One such approach is to treat teams as individual players that duel against the other players on the opposing teams and then apply the average of the duels. This is the "duelling heuristic" mentioned in the TrueSkill paper. I implemented it in the accompanying project. It's ok, but seems a bit too hackish and doesn't converge well.
- Draws - Elo treats draws as a half win and half loss. This doesn't seem fair because draws can tell you a lot. Draws imply you were evenly paired whereas a win indicates you're better, but unsure how much better. Likewise, a loss indicates you did worse, but you don't really know how much worse. So it seems that a draw is important to explicitly model.
The TrueSkill algorithm generalizes Elo by keeping track of two variables: your average (mean) skill and the system's uncertainty about that estimate (your standard deviation). It does this instead of relying on a something like a fixed K-factor. Essentially, this gives the algorithm a dynamic k-factor. This addresses the newbie problem because it removes the need to have "provisional" games. In addition, it addresses the other problems in a nice statistical manner. Tracking these two values are so fundamental to the algorithm that Microsoft researchers informally referred to it as the μσ (mu-sigma) system until the marketing guys gave it the name TrueSkill.
We'll go into the details shortly, but it's helpful to get a quick visual overview of what TrueSkill does. Let's say we have Eric, an experienced player that has played a lot and established his rating over time. In addition, we have newbie: Natalia.
Here's what their skill curves might look like before a game:
And after Natalia wins:
Notice how Natalia's skill curve becomes narrower and taller (i.e. makes a big update) while Eric's curve barely moves. This shows that the TrueSkill algorithm thinks that she's probably better than Eric, but doesn't how much better. Although TrueSkill is a little more confident about Natalia's mean after the game (i.e. it's now taller in the middle), it's still very uncertain. Looking at her updated bell curve shows that her skill could be between 15 and 50.
The rest of this post will explain how calculations like this occurred and how much more complicated scenarios can occur. But to understand it well enough to implement it, we'll need to learn a couple of new things.
Bayesian Probability
Most basic statistics classes focus on frequencies of events occurring. For example, the probability of getting a red marble when randomly drawing from a jar that has 3 red marbles and 7 blue marbles is 30%. Another example is that the probability of rolling two dice and getting a total of 7 is about 17%. The key idea in both of these examples is that you can count each type of outcome and then compute the frequency directly. Although helpful in calculating your odds at casino games, "frequentist" thinking is not that helpful with many practical applications, like finding your skill in a team.
A different approach is to think of probability as degree of belief in something. The basic idea is that you have some prior belief and then you observe some evidence that updates your belief leaving you with an updated posterior belief. As you might expect, learning about new evidence will typically make you more certain about your belief.
Let's assume that you're trying to find a treasure on a map. The treasure could be anywhere on the map, but you have a hunch that it's probably around the center of the map and increasingly less likely as you move away from the center. We could track the probability of finding the treasure using the 3D multivariate Gaussian we saw earlier:
Now, let's say that after studying a book about the treasure, you've learned that there's a strong likelihood that treasure is somewhere along the diagonal line on the map. Perhaps this was based on some secret clue. Your clue information doesn't necessarily mean the treasure will be exactly on that line, but rather that the treasure will most-likely be near it. The likelihood function might look like this in 3D:
We'd like to use our prior information and this new likelihood information to come up with a better posterior guess of the treasure. It turns out that we can just multiply the prior and likelihood to obtain a posterior distribution that looks like this:
This is giving us a smaller and more concentrated area to look at.
If you look at most textbooks, you typically just see this information using 2D isoprobability contour plots that we learned about earlier. Here's the same information in 2D:
For fun, let's say we found additional information saying the treasure is along the other diagonal with the following likelihood:
To incorporate this information, we're able to take our last posterior and make that the prior for the next iteration using the new likelihood information to get this updated posterior:
This is a much more focused estimate than our original belief! We could iterate the procedure and potentially get an even smaller search area.
And that's basically all there is to it. In TrueSkill, the buried treasure that we look for is a person's skill. This approach to probability is called "Bayesian" because it was discovered by a Presbyterian minister in the 1700's named Thomas Bayes who liked to dabble in math.
The central ideas to Bayesian statistics are the prior, the likelihood, and the posterior. There's detailed math that goes along with this and is in the accompanying paper, but understanding these basic ideas is more important:
"When you understand something, then you can find the math to express that understanding. The math doesn't provide the understanding."— Lamport
Bayesian methods have only recently become popular in the computer age because computers can quickly iterate through several tedious rounds of priors and posteriors. Bayesian methods have historically been popular inside of Microsoft Research (where TrueSkill was invented). Way back in 1996, Bill Gates considered Bayesian statistics to be Microsoft Research's secret sauce.
As we'll see later on, we can use the Bayesian approach to calculate a person's skill. In general, it's highly useful to update your belief based off previous evidence (e.g. your performance in previous games). This usually works out well. However, sometimes "Black Swans" are present. For example, a turkey using Bayesian inference would have a very specific posterior distribution of the kindness of a farmer who feeds it every day for 1000 days only to be surprised by a Thanksgiving event that was so many standard deviations away from the turkey's mean belief that he never would have saw it coming. Skill has similar potential for a "Thanksgiving" event where an average player beats the best player in the world. We'll acknowledge that small possibility, but ignore it to simplify things (and give the unlikely winner a great story for the rest of his life).
TrueSkill claims that it is Bayesian, so you can be sure that there is going to be a concept of a prior and a likelihood in it— and there is. We're getting closer, but we still need to learn a few more details.
The Marginalized, but Not Forgotten Distribution
Next we need to learn about "marginal distributions", often just called "marginals." Marginals are a way of distilling information to focus on what you care about. Imagine you have a table of sales for each month for the past year. Let's say that you only care about total sales for the year. You could take out your calculator and add up all the sales in each month to get the total aggregate sales for the year. Since you care about this number and it wasn't in the original report, you could add it in the margin of the table. That's roughly where "margin-al" got its name.
Wikipedia has a great illustration on the topic: consider a guy that ignores his mom's advice and never looks both ways when crossing the street. Even worse, he's too engrossed in listening to his iPod that he doesn't look any way, he just always crosses.
What's the probability of him getting hit by a car at a specific intersection? Let's simplify things by saying that it just depends on whether the light is red, yellow, or green.
|Light State
|Probability of getting hit given light state
This is helpful, but it doesn't tell us what we want. We also need to know how long the light stays a given color
|Light color
|% Time in Color
There's a bunch of probability data here that's a bit overwhelming. If we join the probabilities together, we'll have a "joint distribution" that's just a big complicated system that tells us too much information.
We can start to distill this information down by calculating the probability of getting hit given each light state:
|Total Probability of Getting Hit
|1%*60% = 0.6%
|9%*10% = 0.9%
|90%*30% = 27%
In the right margin of the table we get the value that really matters to this guy. There's a 28.5% marginal probability of getting hit if the guy never looks for cars and just always crosses the street. We obtained it by "summing out" the individual components. That is, we simplified the problem by eliminating variables and we eliminated variables by just focusing on the total rather than the parts.
This idea of marginalization is very general. The central question in this article is "computing your skill," but your skill is complicated. When using Bayesian statistics, we often can't observe something directly, so we have to come up with a probability distribution that's more complicated and then "marginalize" it to get the distribution that we really want. We'll need to marginalize your skill by doing a similar "summing-out" procedure as we did for the reckless guy above.
But before we do that, we need to learn another technique to make calculations simpler.
What's a Factor Graph, and Why Do I Care?
Remember your algebra class when you worked with expressions like this?
Your teacher showed you that you could simplify this by "factor-ing" out w, like this:
We often factor expressions to make them easier to understand and to simplify calculations. Let's replace the variables above with w=4, x=1, y=2, and z=3.
Let's say the numbers on our calculator are circles and the operators are squares. We could come up with an "expression tree" to describe the calculation like this:
You can tell how tedious this computation is by counting 11 "buttons" we'd have to push. We could also factor it like this
This "factorization" has a total of 7 buttons, a savings of 4 buttons. It might not seem like much here, but factorizing is a big idea.
We face a similar problem of how to factor things when we're looking to simplify a complicated probability distribution. We'll soon see how your skill is composed of several "factors" in a joint distribution. We can simplify computations based on how variables are related to these factors. We'll break up the joint distribution into a bunch of factors on a graph. This graph that links factors and variables is called a "factor graph."
The key idea about a factor graph is that we represent the marginal conditional probabilities as variables and then represent each major function of those variables as a "factor." We'll take advantage of how the graph "factorizes" and imagine that each factor is a node on a network that's optimized for efficiency. A key efficiency trick is that factor nodes send "messages" to other nodes. These messages help simplify further marginal computations. The "message passing" is very important and thus will be highlighted with arrows in the upcoming graphs; gray arrows represent messages going "down" the graph and black show messages coming "up" the graph.
The accompanying code and math paper go into details about exactly how this happens, but it's important to realize the high level idea first. That is, we want to look at all the factors that go into creating the likelihood function for updating a person's skill based on a game outcome. Representing this information in a factor graph helps us see how things are related.
Now we have all the foundational concepts that we're ready for the main event: the TrueSkill factor graph!
Enough Chess, Let's Rank Something Harder!
The TrueSkill algorithm is Bayesian because it's composed of a prior multiplied by a likelihood. I've highlighted these two components in the sample factor graph from the TrueSkill paper that looks scary at first glance:
This factor graph shows the outcome of a match that had 3 teams all playing against each other. The first team (on the left) only has one player, but this player was able to defeat both of the other teams. The second team (in the middle) had two players and this team tied the third team (on the right) that had just one player.
In TrueSkill, we just care about a player's marginal skill. However, as is often the case with Bayesian models, we have to explicitly model other things that impact the variable we care about. We'll briefly cover each factor (more details are in the code and math paper).
Factor #1: What Do We Already Know About Your Skill?
The first factor starts the whole process. It's where we get a player's previous skill level from somewhere (e.g. a player database). At this point, we add some uncertainty to your skill's standard deviation to keep game dynamics interesting and prevent the standard deviation from hitting zero since the rest of algorithm will make it smaller (since the whole point is to learn about you and become more certain).
There is a factor and a variable for each player. Each factor is a function that remembers a player's previous skill. Each variable node holds the current value of a player's skill. I say "current" because this is the value that we'll want to know about after the whole algorithm is completed. Note that the message arrow on the factor only goes one way; we never go back to the prior factor. It just gets things going. However, we will come back to the variable.
But we're getting ahead of ourselves.
Factor #2: How Are You Going To Perform?
Next, we add in beta (β). You can think of beta as the number of skill points to guarantee about an 80% chance of winning. The TrueSkill inventors refer to beta as defining the length of a "skill chain."
The skill chain is composed of the worst player on the far left and the best player on the far right. Each subsequent person on the skill chain is "beta" points better and has an 80% win probability against the weaker player. This means that a small beta value indicates a high-skill game (e.g. Go) since smaller differences in points lead to the 80%:20% ratio. Likewise, a game based on chance (e.g. Uno) is a low-skill game that would have a higher beta and smaller skill chain.
Factor #3: How is Your Team Going to Perform?
Now we're ready for one of the most controversial aspects of TrueSkill: computing the performance of a team as a whole. In TrueSkill, we assume the team's performance is the sum of each team member's performance. I say that it's "controversial" because some members of the team probably work harder than others. Additionally, sometimes special dynamics occur that make the sum greater than the parts. However, we'll fight the urge to make it much more complicated and heed Makridakis's advice:
"Statistically sophisticated or complex methods do not necessarily provide more accurate forecasts than simpler ones"
One cool thing about this factor is that you can weight each team member's contribution by the amount of time that they played. For example, if two players are on a team but each player only played half of the time (e.g. a tag team), then we would treat them differently than if these two players played the entire time. This is officially known as "partial play." Xbox game titles report the percentage of time a player was active in a game under the "X_PROPERTY_PLAYER_PARTIAL_PLAY_PERCENTAGE" property that is recorded for each player (it defaults to 100%). This information is used by TrueSkill to perform a fairer update. I implemented this feature in the accompanying source code.
Factor #4: How'd Your Team Compare?
Next, we compare team performances in pairs. We do this by subtracting team performances to come up with pairwise differences:
This is similar to what we did earlier with Elo and subtracting curves to get a new curve.
Factor #5: How Should We Interpret the Team Differences?
The bottom of the factor graph contains a comparison factor based on the team performance differences we just calculated:
The comparison depends on whether the pairwise difference was considered a "win" or a "draw." Obviously, this depends on the rules of the game. It's important to realize that TrueSkill only cares about these two types of results. TrueSkill doesn't care if you won by a little or a lot, the only thing that matters is if you won. Additionally, in TrueSkill we imagine that there is a buffer of space called a "draw margin" where performances are equivalent. For example, in Olympic swimming, two swimmers can "draw" because their times are equivalent to 0.01 seconds even though the times differ by several thousandths of a second. In this case, the "draw margin" is relatively small around 0.005 seconds. Draws are very common in chess at the grandmaster level, so the draw margin would be much greater there.
The output of the comparison factor directly relates to how much your skill's mean and standard deviation will change.
The exact math involved in this factor is complicated, but the core idea is simple:
- Expected outcomes cause small updates because the algorithm already had a good guess of your skill. - Unexpected outcomes (upsets) cause larger updates to make the algorithm more likely to predict the outcome in the future.
The accompanying math paper goes into detail, but conceptually you can think of the performance difference as a number on the bottom (x-axis) of a graph. It represents the difference between the expected winner and the expected loser. A large negative number indicates a big upset (e.g. an underdog won) and a large positive number means the expected person won. The exact update of your skill's mean will depend on the probability of a draw, but you can get a feel for it by looking at this graph:
Similarly, the update to a skill's standard deviation (i.e. uncertainty) depends on how expected the outcome was. An expected outcome shrinks the uncertainty by a small amount (e.g. we already knew it was going to happen). Likewise, an unexpected outcome shrinks the standard deviation more because it was new information that we didn't already have:
One problem with this comparison factor is that we use some fancy math that just makes an approximation (a good approximation, but still an approximation). We'll refine the approximation in the next step.
The Inner Schedule: Iterate, Iterate, Iterate!
We can make a better approximation of the team difference factors by passing around the messages that keep getting updated in the following loop:
After a few iterations of this loop, the changes will be less dramatic and we'll arrive at stable values for each marginal.
Enough Already! Give Me My New Rating!
Once the inner schedule has stabilized the values at the bottom of the factor graph, we can reverse the direction of each factor and propagate messages back up the graph. These reverse messages are represented by black arrows in the graph of each factor. Each player's new skill rating will be the value of player's skill marginal variable once messages have reached the top of the factor graph.
By default, we give everyone a "full" skill update which is the result of the above procedure. However, there are times when a game title might want to not make the match outcome count much because of less optimal playing conditions (e.g. there was a lot of network lag during the game). Games can do this with a "partial update" that is just a way to apply only a fraction of the full update. Game titles specify this via the X_PROPERTY_PLAYER_SKILL_UPDATE_WEIGHTING_FACTOR variable. I implemented this feature in the accompanying source code and describe it in the math paper.
There are some more details left, but we'll stop for now. The accompanying math paper and source code fill in most of the missing pieces. One of the best ways to learn the details is to implement TrueSkill yourself. Feel free to create a port of the accompanying project in your favorite language and share it with the world. Writing your own implementation will help solidify all the concepts presented here.
The most rewarding part of implementing the TrueSkill algorithm is to see it work well in practice. My coworkers have commented on how it's almost "eerily" accurate at computing the right skill for everyone relatively quickly. After several months of playing foosball, the top of the leaderboard (sorted by TrueSkill: the mean minus 3 standard deviations) was very stable. Recently, a very good player started playing and is now the #2 player. Here's a graph of the most recent changes in TrueSkill for the top 5 (of around 40) foosball players:
(Note: Look how quickly the system detected how good this new #2 player is even though his win ratio is right at 50%)
Another interesting aspect of implementing TrueSkill is that it has raised an awareness of ratings among players. People that otherwise wouldn't have played together now occasionally play each other because they know they're similarly matched and will have a good game. One advantage of TrueSkill is that it's not that big of a deal to lose to a much better player, so it's still ok to have unbalanced games. In addition, having ratings has been a good way to judge if you're improving in ability with a new shot technique in foosball or learning more chess theory.
Fun Things from Here
The obvious direction to go from here is to add more games to the system and see if TrueSkill handles them equally well. Given that TrueSkill is the default ranking system on Xbox live, this will probably work out well. Another direction is to see if there's a big difference in TrueSkill based on position in a team (e.g. midfield vs. goalie in foosball). Given TrueSkill's sound statistics based on ranking and matchmaking, you might even have some success in using it to decide between to several options. You could have each option be a "player" and decide each "match" based on your personal whims of the day. If nothing else, this would be an interesting way to pick your next vacation spot or even your child's name.
If you broaden the scope of your search to using the ideas that we've learned along the way, there's a lot more applications. Microsoft's AdPredictor (i.e. the part that delivers relevant ads on Bing) was created by the TrueSkill team and uses similar math, but is a different application.
As for me, it was rewarding to work with an algorithm that has fun social applications as well as picking up machine learning tidbits along the way. It's too bad all of that didn't help me hit the top of any of the leaderboards.
Oh well, it's been a fun journey. I'd love to hear if you dived into the algorithm after reading this and would especially appreciate any updates to my code or other language forks.
- The Math Behind TrueSkill - A math-filled paper that fills in some of the details left out of this post. - Moserware.Skills Project on GitHub - My full implementation of Elo and TrueSkill in C#. Please feel free to create your own language forks. - Microsoft's online TrueSkill Calculators - Allows you to play with the algorithm without having to download anything. My implementation matches the results of these calculators.
Special thanks to Ralf Herbrich, Tom Minka, and Thore Graepel on the TrueSkill team at Microsoft Research Cambridge for their help in answering many of my detailed questions about their fascinating algorithm.


text:
Treatment Resources
Treatment Resources
BPD Treatment Resources
Dialectical Behavior Therapy (DBT) is a cognitive behavioral therapy developed by Marsha M. Linehan, Ph.D., a researcher at the University of Washington, designed to treat persons with borderline personality disorder. For a list of DBT Therapists as well as trainings offered on DBT, go to www.behavioraltech.org
Borderline Personality Resource Center is a free referral center that has a list of mental health professionals across the country. For more information, go to www.bpdresourcecenter.org or call 914.682.5496.


text:
A festival designed to introduce girls to music production and support women already in the field, drew more than 200 participants to Barnard's campus in September for a day of workshops, discussions, and performances. Organized by Barnard Center for Research on Women Alumnae Fellow Ebonie Smith '07, the Gender Amplified Music Festival featured classes on such topics as how to create music using turntables and smart phones, and how to get music published. Also included were discussions on how women can own studios, and gender justice in hip-hop music. The young women attended through several community organizations, including Black Girls Rock!, The Sadie Nash Project, and The Fiver Children's Foundation.
Speakers included engineer, producer, and studio owner Abhita Austin, who has worked with such artists as Missy Elliot; artist and producer Erica Glyn; and producer Pri the Honeydark who did a discussion/performance with producer, activist, and hip-hop artist Invincible. The day finished with a concert by THEESatisfaction. While still a Barnard student, Smith founded Gender Amplified as a way to develop a pipeline for women who want to work behind the scenes in the mostly-male music industry. For more, see genderamplified.com.


text:
NSA documents add more detail to plan to destabilize Middle East
Editor's note: The validity of the document mentioned below cannot be verified due to the exclusivity of the Snowden cache. Cryptome sent a letter to various sources in possession of the documents, including The New York Times, Washington Post, The Guardian, Barton Gellman, Laura Poitrias, Glenn Greenwald, ACLU, EFF and others demanding an accounting. The allegation about ISIS and al-Baghdadi, however, pairs up with other information demonstrating ISIS is an intelligence asset.
According to a document recently released by NSA whistleblower Edward Snowden, Abu Bakr al-Baghdadi, the leader of ISIS, now the Islamic State, is an intelligence asset.
The NSA document reveals the United States, Israel, and Britain are responsible for the creation of ISIS.
Earlier this month Nabil Na'eem, the founder of the Islamic Democratic Jihad Party and former top al-Qaeda commander, told the Beirut-based pan-Arab TV station al-Maydeen all current al-Qaeda affiliates, including ISIS, work for the CIA.
ISIS is a well-armed and trained terrorist group now in control of large areas of Iraq and Syria.
The NSA document states the group was established by U.S., British and Israeli intelligence as part of a strategy dubbed "the hornet's nest" to draw Islamic militants from around the world to Syria.
Prior Evidence of al-Baghdadi Link to Intelligence and Military
Abu Bakr al-Baghdadi "took intensive military training for a whole year in the hands of Mossad, besides courses in theology and the art of speech," the documents explain, according to Gulf Daily News, a Bahrainian source.
In June a Jordanian official told Aaron Klein of WorldNetDaily ISIS members were trained in 2012 by U.S. instructors working at a secret base in Jordan. In 2012 it was reported the U.S., Turkey and Jordan were running a training base for the Syrian rebels in the Jordanian town of Safawi.
[youtube https://www.youtube.com/watch?v=7FVywIgR0Ns]
Corporate media has added weight to myth of Abu Bakr al-Baghdadi, repeatedly exposed as an intelligence asset.
Al-Baghdadi was reportedly a "civilian internee" at Camp Bucca, a U.S. military detention facility near Umm Qasr, Iraq. James Skylar Gerrond, a former U.S. Air Force security forces officer and a compound commander at Camp Bucca in 2006 and 2007, said earlier this month the camp "created a pressure cooker for extremism."
"Circumstantial evidence suggests that al-Baghdadi may have been mind-controlled while held prisoner by the US military in Iraq," writes Dr. Kevin Barrett.
Creating a Fake Terror Threat
The hornet's nest strategy was designed to create the perception that Israel is threatened by an enemy near its borders.
According to the personal diary of former Israeli prime minister Moshe Sharett, however, Israel never took seriously an Arab or Muslim threat to its national security.
"Sharett's diary reveals in explicit language that the Israeli political and military leadership never believed in any Arab danger to Israel," writes Ralph Schoenman. "They sought to maneuver and force the Arab states into military confrontations which the Zionist leadership were certain of winning so Israel could carry out the destabilization of Arab regimes and the planned occupation of additional territory."
In 1982 Oded Yinon, an Israeli journalist with links to the Israeli Foreign Ministry, wrote The Zionist Plan for the Middle East.
The white paper proposed "that all the Arab states should be broken down, by Israel, into small units" and the "dissolution of Syria and Iraq later on into ethnically or religiously unique areas such as in Lebanon, is Israel's primary target on the Eastern front in the long run."
The destruction of the Arab and Muslim states, Yinon suggested, would be accomplished from within by exploiting their internal religious and ethnic tensions.
For more background see our ISIS and the Plan to Balkanize the Middle East.


text:
BUSINESS MEETING: To consider pending calendar business
November 21, 2013
SD-366 Senate Dirksen Building
The purpose of this business meeting is to consider the following bills:
- S. 258, a bill to amend the Federal Land Policy and Management Act of 1976 to improve the management of grazing leases and permits, and for other purposes
- S. 364, a bill to establish the Rocky Mountain Front Conservation Management Area, to designate certain Federal land as wilderness, and to improve the management of noxious weeds in the Lewis and Clark National Forest, and for other purposes
- S.715, a bill to authorize the Secretary of the Interior to use designated funding to pay for construction of authorized rural water projects, and for other purposes
- S. 782, a bill to amend Public Law 101-377 to revise the boundaries of Gettysburg National Military Park to include the Gettysburg Train Station, and for other purposes
- S. 974, a bill to provide for certain land conveyances in the State of Nevada, and for other purposes
- S. 995, a bill to authorize the National Desert Storm Memorial Association to establish the National Desert Storm and Desert Shield Memorial as a commemorative work in the District of Columbia, and for other purposes
- S. 1044, a bill to direct the Secretary of the Interior to install in the area of the World War II Memorial in the District of Columbia a suitable plaque or an inscription with the words that President Franklin D. Roosevelt prayed with the United States on D-Day, June 6, 1944
- S. 1252, a bill to amend the Wild and Scenic Rivers Act to designate segments of the Missisquoi River and the Trout River in the State of Vermont, as components of the National Wild and Scenic Rivers System
- H.R. 507, an Act to provide for the conveyance of certain land inholdings owned by the United States to the Pascua Yaqui Tribe of Arizona, and for other purposes
- H.R. 697, an Act to provide for the conveyance of certain Federal land in Clark County, Nevada, for the environmental remidiation and reclamation of the Three Kids Mine Project Site, and for other purposes
- H.R. 862, an Act to authorize the conveyance of two small parcels of land within the boundaries of the Coconino National Forest containing private improvements that were developed based upon the reliance of the landowners in an erroneous survey conducted in May 1960
- H.R. 876, an Act to authorize the continued use of certain water diversions located on National Forest System land in the Frank Church-River of No Return Wilderness and the Selway-Bitterroot Wilderness in the State of Idaho, and for other purposes
- H.R. 1033, an Act to authorize the acquisition and protection of nationally significant battlefields and associated sites of the Revolutionary War and the War of 1812 under the American Battlefield Protection Program
The meeting will be webcast live on the committee's website, and an archived video will be available after the meeting is complete.
Archived Webcast
If you are having trouble viewing this hearing, please try the following steps:
- Clear your browser's cache - Guide to clearing browser cache
- Close and re-open your browser
- If the above two steps do not help, please try another browser. Internet Explorer or Microsoft Edge have the highest level of compatibility with our player.