Approaches to goal recognition have progressively relaxed the requirements about the amount of domain knowledge and available observations, yielding accurate and efficient algorithms capable of recognizing goals. However, to recognize goals in raw data, recent approaches require either human engineered domain knowledge, or samples of behavior that account for almost all actions being observed to infer possible goals. This is clearly too strong a requirement for real-world applications of goal recognition, and we develop an approach that leverages advances in recurrent neural networks to perform goal recognition as a classification task, using encoded plan traces for training. We empirically evaluate our approach against the state-of-the-art in goal recognition with image-based domains, and discuss under which conditions our approach is superior to previous ones.

Alphabet is broken out into its core Google business and a number of other subsidiaries, which it deems "Other Bets." The majority of Google's business comes from advertising revenues, which the company generates through its search engine as well as a number of other Google-affiliated and partnership websites. Outside of search and advertising, Google generates revenue from products including cloud and enterprise, consumer hardware, mapping, and YouTube. In addition to Google, Alphabet encompasses a host of other subsidiaries called "Other Bets." These companies are more experimental in nature, and as a result are not material to Alphabet's bottom line.

A new internet game called Momo is challenging users to commit suicide. The game originated on Facebook and is now circulating on WhatsApp. Microsoft is clamping down on the sick "Momo suicide challenge," which recently infiltrated the wildly popular online game "Minecraft." The tech giant owns "Minecraft" developer Mojang. The vile "Momo suicide game" has been garnering attention after spreading on WhatsApp, prompting police warnings.

"With respect to the first two, no migratory entry or exit from our country has been registered," Migrations Superintendent Eduardo Sevilla told a news conference. Peru will check if the military officials have entered Peru and proceed in accordance with "the laws in place," Sevilla added, without offering specifics.

An importance sampling and bagging approach to solving the support vector machine (SVM) problem in the context of large databases is presented and evaluated. Our algorithm builds on the nearest neighbors ideas presented in Camelo at al. (2015). As in that reference, the goal of the present proposal is to achieve a faster solution of the SVM problem without a significance loss in the prediction error. The performance of the methodology is evaluated in benchmark examples and theoretical aspects of subsample methods are discussed.

Maria Pocovi slides her laptop over to me with the webcam switched on. My face stares back at me, overlaid with a grid of white lines that map the contours of my expression. Next to it is a shaded window that tracks six "core emotions": happiness, surprise, disgust, fear, anger, and sadness. Each time my expression shifts, a measurement bar next to each emotion fluctuates, as if my feelings were an audio signal. When I look back at Pocovi, I get the sense she knows exactly what I'm thinking with one glance. Petite with a welcoming smile, Pocovi, the founder of Emotion Research Lab in Valencia, Spain, is a global entrepreneur par excellence.

In a café on the Upper East Side of Manhattan, a one-time videogame developer turned political theorist named Santiago Siri is trying to explain to me how his nonprofit startup, Democracy.Earth, aims to fix the world's broken politics with the help of the blockchain. The conversation has already covered a dizzying amount of ground. We've discussed the emergence of the Westphalian order of nation-states in the 17th century, Russia's interference in the 2016 US election, the total collapse of Venezuelan society, and Siri's own experience of political corruption in his native Argentina. But he finally boils it all down to one short sentence. "We want to tokenize the like," Siri says.

If I wanted to show my distaste for the direction the US is going by boycotting American PC hardware, software and services, could it be done? You could certainly eliminate a lot of American products, but you might be giving up features without getting any ethical benefits. For example, more than a billion people already manage without a lot of American technology because they live in China or Russia. While I share your distaste for the Trump regime, Xi Jinping and Vladimir Putin are not exactly choirboys. And while Trump is scapegoating immigrants, more than half of America's top technology companies were co-founded by immigrants or the children of immigrants.

Maduro's critics say he is using the incident to stifle dissent and cement his power in the oil-rich nation amid the economic crisis that has brought with it hyperinflation and power cuts. He says his government is the victim of an "economic war" led by opposition activists with the help of Washington.

The Dynamic Chain Event Graph (DCEG) is able to depict many classes of discrete random processes exhibiting asymmetries in their developments and context-specific conditional probabilities structures. However, paradoxically, this very generality has so far frustrated its wide application. So in this paper we develop an object-oriented method to fully analyse a particularly useful and feasibly implementable new subclass of these graphical models called the N Time-Slice DCEG (NT-DCEG). After demonstrating a close relationship between an NT-DCEG and a specific class of Markov processes, we discuss how graphical modellers can exploit this connection to gain a deep understanding of their processes. We also show how to read from the topology of this graph context-specific independence statements that can then be checked by domain experts. Our methods are illustrated throughout using examples of dynamic multivariate processes describing inmate radicalisation in a prison.