Horrifying next twist in the Alexander brothers case: MAUREEN CALLAHAN exposes an unthinkable perversion that's been hiding in plain sight Alexander brothers' alleged HIGH SCHOOL gang rape video: Classmates speak out on sick'taking turns' footage... as creepy unseen photos are exposed Model Cindy Crawford, 60, mocked for her'out of touch' morning routine: 'Nothing about this is normal' Kentucky mother and daughter turn down $26.5MILLION to sell their farms to secretive tech giant that wants to build data center there Tucker Carlson erupts at Trump adviser as she hurls'SLANDER' claim linking him to synagogue shooting NFL superstar Xavier Worthy spills all on Travis Kelce, the Chiefs' struggles... and having Taylor Swift as his No 1 fan Heartbreaking video shows very elderly DoorDash driver shuffle down customer's driveway with coffee order because he is too poor to retire Amber Valletta, 52, was a '90s Vogue model who made movies with Sandra Bullock and Kate Hudson, see her now Nancy Mace throws herself into Iran warzone as she goes rogue on Middle East rescue mission: 'I AM that person' Hidden toxins in kids' treats EXPOSED: Health guru Jillian Michaels' sit-down with Casey DeSantis reveals dangers lurking in popular foods Declassified CIA files reveal chilling blueprint to manipulate Americans' minds through covert drugging with vaccines READ MORE: CIA mind-control project'programmed Trump shooter', congressman claims A newly released CIA document reveals a chilling blueprint to manipulate minds through covert drugging experiments . The report, added to the CIA's reading room in 2025, details the government's once top-secret Project Artichoke that ran from 1951 to 1956, focusing on behavior control, interrogation techniques and psychological manipulation. The seven-page document, titled'Special Research for Artichoke,' with an attachment labeled'Suggested Fields for Special Research Relative Artichoke,' outlines proposals to develop chemicals capable of altering human behavior. It discusses drugs designed for both immediate effects, like truth serums and long-term influence, potentially administered through food, water, alcohol or cigarettes. Researchers also suggested that such substances could be disguised in medical treatments such as vaccinations or injections.

The capabilities of Google's artificial intelligence are staggering. The phantoms within Google Photos can organize your pictures, the wizards inside Google Docs let you type and edit using spoken commands, and the brilliant pixies powering AlphaGo can easily beat a master of a 2,500-year-old game more complex than chess. Google's new Smart Reply feature uses artificial neural networks to come up with appropriate responses to email messages. Whether you're using Google's sleek email manager in the browser or via mobile app, Inbox can now answer your emails for you. The machine replies aren't sent automatically, which is good.

The factorial size of the space of rankings, however, typically forces one to make structural assumptions, such as smoothness, sparsity, or probabilistic independence about these underlying distributions. We approach the modeling problem from the computational principle that one should make structural assumptions which allow for ecient calculation of typical probabilistic queries. For ranking models, typical queries predominantly take the form of partial ranking queries (e.g., given a user's top-k fa In this paper, we argue that ried independence factorizations proposed in recent literature [7, 8] are a natural structural assumption for ranking distributions, allowing for particularly ef-cient processing of partial ranking queries. 1 Intr Both problems are challenging because of the fact that, as the number of items being ranked increases, the number of possible rankings increases factorially. The key to ecient representations and reasoning is to identify exploitable problem structure, and to this end, there have been a number of smart structural assumptions proposed by the scientic community. These assumptions have typically been designed to reduce the number of necessary parameters of a model and have ranged from smoothness [10], to sparsity [11], to exponential family parameterizations [14].