The movie was earlier scheduled to be released on 12 July 2013 but was eventually released on 2 August 2013. The date was postponed because of a major release, Bhaag Milkha Bhaag, on 12 July 2013. Upon release, the movie received universal critical acclaim.
Ba Pass Movie Torrent
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This movie was about two married guys who get a "hall pass" from their marriages, granted from their wives. One of them is given to him voluntarily, the other one pretty much forces it out of her. They pretty much have a week to do "as they please" with other women as one of the wives has had enough with his "noticing" other women.While the idea is clever and decently executed, and there were some funny scenes and I liked the "Law & Order" spoof parts, I felt like the raunchy humor and "potty mouthed" jokes damaged it. While that could have helped in other films, it didn't really help here.The cast (especially the leads) was decent and worked well together. You might like this if you are a comedy fan.** out of ****
I didn't realise this was from directing brothers, Bobby and Peter Farrelly (Dumb and Dumber, There's Something About Mary, The Heartbreak Kid), I thought from the title it was going to be a teen school comedy or something, it should be called "Free Pass", just like the term from American Pie: The Naked Mile, but never mind. Basically Rick (Owen Wilson) is married to Maggie (Blades of Glory's Jenna Fischer), and Fred (Jason Sudeikis) is married to Grace (Anchorman's Christina Applegate), both women can tell that the boys are missing the excitement of being single, looking at other women and hating the lack of good sex. With their wives fed up of their behaviour, treating them like trash, both Rick and Fred are granted a "hall pass", a week of non-marriage in which they can do whatever they like, no questions, no holes barred and no consequences during or afterwards. While Fred laps up the idea of being able to have sex with any fit woman he wants, Rick is very dubious about it, since he has been with his wife for so long. It takes a couple days for one to find someone they may actually able to get into bed with, but Rick finds and flirts with attractive waitress Leigh (Nicky Whelan), and when he wakes up naked on her sofa he questions if they did anything, they didn't. Rick also gets an invite from the family babysitter Paige (Alexandra Daddario), but he turns her down for Leigh, but he is stopped from possibly doing anything when he finds out Fred, who got a woman in a hotel room, is in hospital. It should be said while all this is going on, Maggie and Grace hang out and get closer, and they end up having some invitations themselves, but it is obvious they are thinking negative things about the whole week of marriage thing. In the end, love really does prevail as Rick turns down the possibility of sex with Leigh, he is obvious cross at himself, but he loves his wife Maggie much more, and even though Fred did have sex (near enough) with another woman he is forgiven by Grace, everything goes back to being happy and normal. Also starring Richard Jenkins as Coakley, Stephen Merchant as Gary, Larry Joe Campbell as Hog-Head, J.B. Smoove as Flats, Tyler Hoechlin as Gerry, Bruce Thomas as Rick Coleman and Kathy Griffin. The cast all their parts fine, Wilson obviously being the lead did well, and Merchant when he was on had some small giggles to give, the premise is nothing very original, and it lacks the style of comedy other Farrelly brothers films provide, i.e. gross and wrong but right kind of stuff, but it isn't a terrible comedy. Okay!
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Torrenting can be used to download and distribute any type of file. This includes copyrighted material such as music, movies and TV shows, but also free or public content, and content from companies looking to reduce server costs.
You can think of trackers as the phone-books of BitTorrent. When a peer downloads a torrent file (or accesses a magnet link, more on this later), part of that file is the URL needed to connect to the tracker (or multiple trackers). A torrent client then takes that URL and sends a message to the tracker, which provides a list of other peers.
Once two peers are connected, they will use the remaining content in the torrent file (namely a hash that represents the file contents) to identify and exchange the pieces of the file that they are missing. This is significant, as it means the information detailing the actual contents of the files is not located within the tracker, but rather within the torrent file itself.
Once a peer has acquired multiple pieces of a file, they can maintain multiple transfer sessions simultaneously, exchanging pieces with multiple other peers at the same time. This is why most torrents start downloading at low speeds, but get faster as more pieces are acquired.
As we mentioned earlier, all the information you need to access the shared data is located within a torrent file. These are created by the original distributor of the data, who then becomes the original seeder when they upload the file to a hosting site such as The Pirate Bay.
Magnet links contain the same information that torrent files do, namely the tracker URL, hashes of the file pieces and the number of pieces. However, instead of being delivered in a file that your torrent client has to open, magnet links open the torrent client and deliver the information directly, much like how regular web links and web browsers function.
As we covered earlier, a torrent tracker is the place where your torrent client goes in order to find a list of peers for any given torrent. These trackers come in two broad categories: private and public.
That said, in certain jurisdictions, particularly in the U.S., there is a possibility of direct legal action by the copyright holder. This is by no means common, as there are generally less than 5,000 copyright infringement lawsuits filed yearly across the entire U.S., and the vast majority of these are unrelated to torrenting.
The best defense against this is to use well-established torrent sites and trackers, most of which include some sort of rating or reputation system. These let you pick torrent files that have been verified by other users or come from people with proven track records.
Great explanation, thanks !I am confused as to why torrent is prohibited by ISP but why are there some official sites providing torrents. So if we use a torrent on an official site (like: debian os) does it still break the TOS from the ISP?
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"Recent advances in high-throughput technologies have unleashed a torrent of data with a large number of dimensions. Examples include gene expression pattern images, microarray gene expression data, and neuroimages. Variable selection is crucial for the analysis of these data. In this talk, we consider the structured sparse learning for variable selection where the structure over the features can be represented as a hierarchical tree, an undirected graph, or a collection of disjoint or overlapping groups. We show that the proximal operator associated with these structures can be computed efficiently, thus accelerated gradient techniques can be applied to scale structured sparse learning to large-size problems. We demonstrate the efficiency and effectiveness of the presented algorithms using synthetic and real data."
Probabilistic matrix factorization (PMF) is a powerful method for modeling data associated with pairwise relationships. PMF is used in collaborative filtering, computational biology, and document analysis among other application areas. In many of these domains, additional information is available that might assist in the modeling of the pairwise interactions. For example, when modeling movie ratings, we might know when the rating occurred, where the user lives, or what actors appear in the movie. It has been difficult, however, to incorporate this kind of side information into the probabilistic matrix factorization model. I will discuss some recent work to develop a nonparametric Bayesian framework for incorporating side information by coupling together multiple PMF problems via Gaussian process priors. I will show how we have used this model to successfully model the outcomes of NBA basketball games, allowing for the attributes of the teams to vary over time and to include home-team advantage. This is joint work with George Dahl and Iain Murray.
Maximum margin clustering extends the theory of support vector machine to unsupervised learning, and has shown promising performance in recent studies. However, it has three major problems that question its application of real-world applications: (1) it is computationally expensive and difficult to scale to large-scale datasets; (2) it requires data preprocessing to ensure the clustering boundary to pass through the origins, which makes it unsuitable for clustering unbalanced dataset; and (3) its performance is sensitive to the choice of kernel functions. In this paper, we propose the "Generalized Maximum Margin Clustering" framework that addresses the above three problems simultaneously. The new framework generalizes the maximum margin clustering algorithm in that (1) it allows any clustering boundaries including those not passing through the origins; (2) it significantly improves the computational efficiency by reducing the number of parameters; and (3) it automatically determines the appropriate kernel matrix without any labeled data. Our empirical studies demonstrate the efficiency and the effectiveness of the generalized maximum margin clustering algorithm. Furthermore, in this talk, I will show the theoretical connection among the spectral clustering, the maximum margin clustering and the generalized maximum margin clustering. 2ff7e9595c
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