SANAA/ADEN – Suspected U.S. drone strikes have killed three alleged al-Qaida operatives in Yemen's southwestern Bayda province, security and tribal officials said, the first such killings reported in the country since Donald Trump assumed the U.S. presidency Friday. The two Saturday strikes killed Abu Anis al-Abi, an area field commander, and two others, the officials said, speaking on condition of anonymity as they were not authorized to release the information to journalists. U.S. drone strikes against suspected al-Qaida targets have been commonplace in the years since the Sept. 11, 2001, attacks on New York and Washington, as a retaliatory measure against the group. The use of unmanned aircraft as well as airstrikes in the Arab world's poorest country rose dramatically under President Barack Obama, with data from the Britain-based Bureau of Investigative Journalism showing spikes in attacks, especially in 2012 and 2016. On Thursday, U.S. intelligence officials said as many as 117 civilians had been killed in drone and other counterterror attacks in Pakistan, Yemen and elsewhere during Obama's presidency.

One of the ubiquitous representation of long DNA sequence is dividing it into shorter k-mer components. Unfortunately, the straightforward vector encoding of k-mer as a one-hot vector is vulnerable to the curse of dimensionality. Worse yet, the distance between any pair of one-hot vectors is equidistant. This is particularly problematic when applying the latest machine learning algorithms to solve problems in biological sequence analysis. In this paper, we propose a novel method to train distributed representations of variable-length k-mers. Our method is based on the popular word embedding model word2vec, which is trained on a shallow two-layer neural network. Our experiments provide evidence that the summing of dna2vec vectors is akin to nucleotides concatenation. We also demonstrate that there is correlation between Needleman-Wunsch similarity score and cosine similarity of dna2vec vectors.

X-rays are commonly performed imaging tests that use small amounts of radiation to produce pictures of the organs, tissues, and bones of the body. X-rays of the chest are used to detect abnormalities or diseases of the airways, blood vessels, bones, heart, and lungs. In this work we present a stochastic attention-based model that is capable of learning what regions within a chest X-ray scan should be visually explored in order to conclude that the scan contains a specific radiological abnormality. The proposed model is a recurrent neural network (RNN) that learns to sequentially sample the entire X-ray and focus only on informative areas that are likely to contain the relevant information. We report on experiments carried out with more than $100,000$ X-rays containing enlarged hearts or medical devices. The model has been trained using reinforcement learning methods to learn task-specific policies.

Successful human-robot cooperation hinges on each agent's ability to process and exchange information about the shared environment and the task at hand. Human communication is primarily based on symbolic abstractions of object properties, rather than precise quantitative measures. A comprehensive robotic framework thus requires an integrated communication module which is able to establish a link and convert between perceptual and abstract information. The ability to interpret composite symbolic descriptions enables an autonomous agent to a) operate in unstructured and cluttered environments, in tasks which involve unmodeled or never seen before objects; and b) exploit the aggregation of multiple symbolic properties as an instance of ensemble learning, to improve identification performance even when the individual predicates encode generic information or are imprecisely grounded. We propose a discriminative probabilistic model which interprets symbolic descriptions to identify the referent object contextually w.r.t.\ the structure of the environment and other objects. The model is trained using a collected dataset of identifications, and its performance is evaluated by quantitative measures and a live demo developed on the PR2 robot platform, which integrates elements of perception, object extraction, object identification and grasping.

We consider the demixing problem of two (or more) high-dimensional vectors from nonlinear observations when the number of such observations is far less than the ambient dimension of the underlying vectors. Specifically, we demonstrate an algorithm that stably estimate the underlying components under general \emph{structured sparsity} assumptions on these components. Specifically, we show that for certain types of structured superposition models, our method provably recovers the components given merely $n = \mathcal{O}(s)$ samples where $s$ denotes the number of nonzero entries in the underlying components. Moreover, our method achieves a fast (linear) convergence rate, and also exhibits fast (near-linear) per-iteration complexity for certain types of structured models. We also provide a range of simulations to illustrate the performance of the proposed algorithm.

Predictive analytics, the Internet of Things and machine learning are all being touted as top technologies that will improve engagement with customers, even if marketers can't agree which to prioritise, a new international survey reports. The latest Marketo global survey into the future of technology and its impact on marketing found the trio of emerging technologies were top of the list for international marketers when it comes to how to improve customer engagement. However, respondents in different marketers didn't agree on which to prioritise first. Over half of US marketers, for example (57 per cent), believed predictive analytics will be the primary technology to leverage in order to best engage with customers, compared to only 11 per cent of international marketers. The report also found 37 per cent of all surveyed saw reporting and analytics as a top priority, with US marketers leading the pack (42 per cent versus 34 per cent).

Would you like to translate terabytes of data into unforgettable holidays for millions of people around the globe? Booking.com, the world's largest accommodation booking website, is looking for rock star Data Scientists to add to join our highly successful Personalization Team within the Front End department. This product development team crunches endless amounts of data to provide our customers with the best possible experience. They focus on anything from understanding and predicting market data, to ranking all properties on our website, and providing our customers with the most relevant personalized recommendations. As a Data Scientist you'll work side by side with Developers, Designers and Product Owners, and take full ownership of your work – from the initial idea-generation phase to the implementation of the final product on our website. Our ideal candidate is result-focused, innovative and has solid quantitative background and a good business understanding.

As business, policy, and technology leaders gathered at the annual World Economic Forum in Davos, Switzerland this year, the rise of populism with Brexit in the UK and the election of Donald Trump in the US drove discussions about the pros and cons of globalization. While globalization has improved the living conditions of vast swaths of this planet's population, it has also led to shifting employment patterns, as jobs leave the US for China and other low-wage countries. However, the Davos cognoscenti believe wage inequality is only part of the problem. Hand-in-hand with globalization is the topic of automation – seen as more of a culprit for increasing inequality than the usual scapegoat, low-cost labor. "[Automation] particularly represents a challenge for people in our economy with low skills, particularly the older workers who don't feel able to embrace and learn new skills and new technologies," says Philip Hammond, the UK Chancellor of the Exchequer, according to an article in the Washington Post.

Cancer is a devastating disease and statistics now suggest that nearly one in every two people worldwide will develop cancer at some point in their lives. Although there are various treatments available that are improving all the time, there is still no cure for cancer. Research in developing effective cancer treatments has been going on for decades, and now researchers are turning to the help of AI in hopes of finding a cure for the disease. But one thing that is needed for searching for a cure for cancer, even when using AI, is data. The problem with this is that a lot of data including mammograms, genetic tests, and medical records are still under lock and key and not available to those who can make use of it. However, on the flip side, there are now a number of large scale initiatives in place that are willing to share data and make it easy to do so.

Why is it that every time humans develop a really clever computer system in the movies, it seems intent on killing every last one of us at its first opportunity? In Stanley Kubrick's masterpiece, 2001: A Space Odyssey, HAL 9000 starts off as an attentive, if somewhat creepy, custodian of the astronauts aboard the USS Discovery One, before famously turning homicidal and trying to kill them all. In The Matrix, humanity's invention of AI promptly results in human-machine warfare, leading to humans enslaved as a biological source of energy by the machines. In Daniel H. Wilson's book Robopocalypse, computer scientists finally crack the code on the AI problem, only to have their creation develop a sudden and deep dislike for its creators. Is Siri just a few upgrades away from killing you in your sleep? And you're not an especially sentient being yourself if you haven't heard the story of Skynet (see The Terminator, T2, T3, etc.) The simple answer is that -- movies like Wall-E, Short Circuit, and Chappie, notwithstanding -- Hollywood knows that nothing guarantees box office gold quite like an existential threat to all of humanity. Whether that threat is likely in real life or not is decidedly beside the point. How else can one explain the endless march of zombie flicks, not to mention those pesky, shark-infested tornadoes? The reality of AI is nothing like the movies. Siri, Alexa, Watson, Cortana -- these are our HAL 9000s, and none seems even vaguely murderous. The technology has taken leaps and bounds in the last decade, and seems poised to finally match the vision our artists have depicted in film for decades. Is Siri just a few upgrades away from killing you in your sleep, or is Hollywood running away with a tired idea? Looking back at the last decade of AI research helps to paint a clearer picture of a sometimes frightening, sometimes enlightened future. An increasing number of prominent voices are being raised about the real dangers of humanity's continuing work on so-called artificial intelligence.