British politicians who examined the details of a drone strike which killed a British man in Syria said they were disappointed by the government's lack of transparency during investigations. On August 21, 2015, the UK conducted a drone strike in Raqqa for the first time outside the traditional theatre of war, killing 21-year-old British national Reyaad Khan, a suspected fighter with the Islamic State of Iraq and the Levant (ISIL, also known as ISIS), and two other people. "We are in no doubt that Reyaad Khan posed a very serious threat to the UK," the Intelligence and Security Committee in the UK said in a report on Wednesday. "There is nevertheless a question as to how the threat is quantified and in this instance whether the actions of Khan and his associates amounted to an'armed attack' against the UK or Iraq - which is clearly a subjective assessment," the committee said. "The [government's] failure to provide what we consider to be relevant documents is profoundly disappointing," the report added.

Between Walking Dead and its spinoff, Fear the Walking Dead, AMC currently has a cable ratings juggernaut on its hands. But before the network put all its eggs in the zombie basket, it was committed to developing critical darling successors to shows like Mad Men and Breaking Bad. The would-be fill-in for the latter, Low Winter Sun, got cancelled after a single botched season. But Halt and Catch Fire, a darkly lit drama following Joe MacMillan (Lee Pace), a tortured genius businessman in the fledgling world of personal computers in 1980s Texas, rebounded from anemic early ratings to earn increasingly improbable renewals for a second and then third season. And that's when the show did something pretty remarkable: It got even better.

Many forms of interactive digital entertainment involve interacting with virtual dramatic characters. Our long term goal is to procedurally generate character dialogue behavior that automatically mimics, or blends, the style of existing characters. In this paper, we show how linguistic elements in character dialogue can define the style of characters in our RPG SpyFeet. We utilize a corpus of 862 film scripts from the IMSDb website, representing 7,400 characters, 664,000 lines of dialogue and 9,599,000 word tokens. We utilize counts of linguistic reflexes that have been used previously for personality or author recognition to discriminate different character types. With classification experiments, we show that different types of characters can be distinguished at accuracies up to 83% over a baseline of 20%. We discuss the characteristics of the learned models and show how they can be used to mimic particular film characters.