In this work we present a novel system for PET estimation using CT scans. We explore the use of fully convolutional networks (FCN) and conditional generative adversarial networks (GAN) to export PET data from CT data. Our dataset includes 25 pairs of PET and CT scans where 17 were used for training and 8 for testing. The system was tested for detection of malignant tumors in the liver region. Initial results look promising showing high detection performance with a TPR of 92.3% and FPR of 0.25 per case. Future work entails expansion of the current system to the entire body using a much larger dataset. Such a system can be used for tumor detection and drug treatment evaluation in a CT-only environment instead of the expansive and radioactive PET-CT scan.

Tesla CEO Elon Musk and Facebook CEO Mark Zuckerberg had a little tit-for-tat this week over artificial intelligence. Does it represent an existential threat to humanity, as Musk argues, or does it hold great promise to improve our lives, as Zuckerberg believes? On a Facebook Live broadcast, Zuckerberg declared, "I think people who are naysayers and try to drum up these doomsday scenarios -- I just, I don't understand it. It's really negative, and in some ways I actually think it is pretty irresponsible." Musk took the kerfuffle to Twitter, replying: "I've talked to Mark about this. His understanding of the subject is limited."

Are our machines turning into gods? Jie, the world's best player of the world's oldest board game, Go, had just met his match… in the form of a program called AlphaGo. In the space of a year, the program had become "almost like the god of Go," said Jie after losing to AlphaGo. Jie had been playing the game, viewed as too hard for machines to excel at, since he was 10. AlphaGo was only made by Google's parent, Alphabet, in 2014.

Late last year, some WikiLeaks supporters were growing concerned: What had happened to Julian Assange? The then-45-year-old founder of the anti-secrecy publisher was no stranger to controversy. Since 2012, he has sheltered in the Ecuadorian Embassy in Knightsbridge, London, following allegations of sexual assault. But the publication of leaked emails from Democratic Party officials in the run-up to the US presidential election saw Assange wield unprecedented influence while at the center of a global media firestorm. After the election, though, suspicions were growing that something had happened to him.

The end of the world as we know it is near. And that's a good thing, according to many of the futurists who are predicting the imminent arrival of what's been called the technological singularity. The technological singularity is the idea that technological progress, particularly in artificial intelligence, will reach a tipping point to where machines are exponentially smarter than humans. It has been a hot topic of late. Well-known futurist and Google engineer Ray Kurzweil (co-founder and chancellor of Singularity University) reiterated his bold prediction at Austin's South by Southwest (SXSW) festival this month that machines will match human intelligence by 2029 (and has said previously the Singularity itself will occur by 2045).

Artificial intelligence (AI) and machine learning play an important role in helping law enforcement deal with increasing threats, but the need then for access to data is likely to further drive concerns about privacy. Closer collaboration between the private and public sectors as well as citizens also would be essential, according to delegates at Interpol World 2017 in Singapore this week. The rise of urbanisation, globalisation, and online connectivity had unleashed tremendous amount of data that was never before available, Anselm Lopez, director of strategic relations directorate, international cooperation and partnerships division at Singapore's Ministry of Home Affairs. He also is part of Interpol's Asia executive committee. Lopez noted that data had become a critical element in decision making for law enforcement, as it had for enterprises, and these agencies would have to adapt or be rendered irrelevant.

The ingredients would include masses of processing power, lots of computer-science boffins, a torrent of capital--and abundant data with which to train machines to recognise and respond to patterns. That environment might sound like a fair description of America, the current leader in the field. But in some respects it is truer still of China. The country is rapidly building up its cloud-computing capacity. For sheer volume of research on AI, if not quality, Chinese academics surpass their American peers; AI-related patent submissions in China almost tripled between 2010 and 2014 compared with the previous five years.

To illustrate his position, he used the example of the MH17 plane crash. "When the Russians shot down the airliner, and we were searching for the smoking gun, we found it a month later -- on Facebook," the general said at an Air Force Association breakfast in Washington Wednesday, according to DefenseTech.com. Goldfein pointed the finger at Russia for the crash, as though the Netherlands have already made a conclusion as to who shot the Buk missile that brought the plane down (which they have not). "We found posted pictures on Russian blog sites that actually showed the activity, but it took us a month to figure that out," Goldfein said, leaning on social media as a source of reliable information, even though investigation of the MH17 catastrophe is still going on and it is hampered by "a great deal of disinformation and attempts to discredit the investigation," Dutch Foreign Minister Bert Koenders said in a statement earlier in July, according to France 24. Discussing a trip to the offices of the Bloomberg news agency, he reportedly asked a technician to perform a Twitter search on violent extremist activity over the last 48 hours, and the system actually mapped the relevant tweets on a map.

In this paper, we present a set of computational methods to identify the likeliness of a word being borrowed, based on the signals from social media. In terms of Spearman correlation coefficient values, our methods perform more than two times better (nearly 0.62) in predicting the borrowing likeliness compared to the best performing baseline (nearly 0.26) reported in literature. Based on this likeliness estimate we asked annotators to re-annotate the language tags of foreign words in predominantly native contexts. In 88 percent of cases the annotators felt that the foreign language tag should be replaced by native language tag, thus indicating a huge scope for improvement of automatic language identification systems.

In this paper, a generalized multivariate Student-t mixture model is developed for classification and clustering of Low Probability of Intercept radar waveforms. A Low Probability of Intercept radar signal is characterized by a pulse compression waveform which is either frequency-modulated or phase-modulated. The proposed model can classify and cluster different modulation types such as linear frequency modulation, non linear frequency modulation, polyphase Barker, polyphase P1, P2, P3, P4, Frank and Zadoff codes. The classification method focuses on the introduction of a new prior distribution for the model hyper-parameters that gives us the possibility to handle sensitivity of mixture models to initialization and to allow a less restrictive modeling of data. Inference is processed through a Variational Bayes method and a Bayesian treatment is adopted for model learning, supervised classification and clustering. Moreover, the novel prior distribution is not a well-known probability distribution and both deterministic and stochastic methods are employed to estimate its expectations. Some numerical experiments show that the proposed method is less sensitive to initialization and provides more accurate results than the previous state of the art mixture models.