It seemed like a gimmick. Beijing has hordes of scooter-riding delivery people to fetch your noodles and dumplings at the swipe of a smartphone app. I gave the robots a try anyway, placing my salad order on a delivery app as usual, but specifying that I wanted a robot in the building to bring it to me. The advantages soon became clear. They don't get impatient or angry, like human delivery people sometimes do, and they don't waste my time asking for directions.

SINGAPORE – High-tech Singapore is planning to roll out a swarm of drones for tasks that include delivering parcels, inspecting buildings and providing security, but safety and privacy concerns mean the initiative may hit turbulence. Companies have already started testing the devices for commercial use, mainly in an area of over 200 hectares (500 acres) dotted with high-rise buildings and shopping malls, specially designated by the government for the trials. It is part of the affluent city's drive to embrace technological innovation, as well as an effort to tackle a manpower shortage in a country of just 5.6 million, which relies on foreign migrant workers in many low-paying sectors. Commercial use of unmanned aerial vehicles is already taking off around the world, in areas as diverse as crop-spraying and surveying for insurance claims, but Singapore's push represents a particularly ambitious bet on the technology. Singapore's Civil Aviation Authority has got behind the project, saying it recognizes the potential for drones "to transform mobility and logistics," and is working with industry players as it seeks to shape regulations for the sector.

Uplift modeling aims to directly model the incremental impact of a treatment on an individual response. It has been widely and successfully used in healthcare analytics and business operations, where one tries to measure the net effect of a new medicine on patients or to understand the impact of a marketing campaign on company revenue. In this work, we address the problem from a new angle and reformulate it as a Markov Decision Process (MDP). This new formulation allows us to handle the lack of explicit labels, to deal with any number of actions (in comparison to the normal two action uplift modeling), and to apply it to applications with responses of general types, which is a challenging task for previous methods. Furthermore, we also design an unbiased metric for more accurate offline evaluation of uplift effects, set up a better reward function for the policy gradient method to solve the problem and adopt some action-based baselines to reduce variance. We conducted extensive experiments on both a synthetic dataset and real-world scenarios, and showed that our method can achieve significant improvement over previous methods.

Deep learning has revolutionized the ability to learn "end-to-end" autonomous vehicle control directly from raw sensory data. While there have been recent advances on extensions to handle forms of navigation instruction, these works are unable to capture the full distribution of possible actions that could be taken and to reason about localization of the robot within the environment. In this paper, we extend end-to-end driving networks with the ability to understand maps. We define a novel variational network capable of learning from raw camera data of the environment as well as higher level roadmaps to predict (1) a full probability distribution over the possible control commands; and (2) a deterministic control command capable of navigating on the route specified within the map. Additionally, we formulate how our model can be used to localize the robot according to correspondences between the map and the observed visual road topology, inspired by the rough localization that human drivers can perform. We evaluate our algorithms on real-world driving data, and reason about the robustness of the inferred steering commands under various types of rich driving scenarios. In addition, we evaluate our localization algorithm over a new set of roads and intersections which the model has never driven through and demonstrate rough localization in situations without any GPS prior.

The authors propose a parametric model called the arena model for prediction in paired competitions, i.e. paired comparisons with eliminations and bifurcations. The arena model has a number of appealing advantages. First, it predicts the results of competitions without rating many individuals. Second, it takes full advantage of the structure of competitions. Third, the model provides an easy method to quantify the uncertainty in competitions. Fourth, some of our methods can be directly generalized for comparisons among three or more individuals. Furthermore, the authors identify an invariant Bayes estimator with regard to the prior distribution and prove the consistency of the estimations of uncertainty. Currently, the arena model is not effective in tracking the change of strengths of individuals, but its basic framework provides a solid foundation for future study of such cases.

Previous studies have shown that deep neural networks (DNNs) with common settings often capture target functions from low to high frequency, which is called Frequency Principle (F-Principle). It has also been shown that F-Principle can provide an understanding to the often observed good generalization ability of DNNs. However, previous studies focused on the loss function of mean square error, while various loss functions are used in practice. In this work, we show that the F-Principle holds for a general loss function (e.g., mean square error, cross entropy, etc.). In addition, DNN's F-Principle may be applied to develop numerical schemes for solving various problems which would benefit from a fast converging of low frequency. As an example of the potential usage of F-Principle, we apply DNN in solving differential equations, in which conventional methods (e.g., Jacobi method) is usually slow in solving problems due to the convergence from high to low frequency.

We address two challenges of probabilistic topic modelling in order to better estimate the probability of a word in a given context, i.e., P(word|context): (1) No Language Structure in Context: Probabilistic topic models ignore word order by summarizing a given context as a "bag-of-word" and consequently the semantics of words in the context is lost. The LSTM-LM learns a vector-space representation of each word by accounting for word order in local collocation patterns and models complex characteristics of language (e.g., syntax and semantics), while the TM simultaneously learns a latent representation from the entire document and discovers the underlying thematic structure. We unite two complementary paradigms of learning the meaning of word occurrences by combining a TM (e.g., DocNADE) and a LM in a unified probabilistic framework, named as ctx-DocNADE. (2) Limited Context and/or Smaller training corpus of documents: In settings with a small number of word occurrences (i.e., lack of context) in short text or data sparsity in a corpus of few documents, the application of TMs is challenging. We address this challenge by incorporating external knowledge into neural autoregressive topic models via a language modelling approach: we use word embeddings as input of a LSTM-LM with the aim to improve the word-topic mapping on a smaller and/or short-text corpus. The proposed DocNADE extension is named as ctx-DocNADEe. We present novel neural autoregressive topic model variants coupled with neural LMs and embeddings priors that consistently outperform state-of-the-art generative TMs in terms of generalization (perplexity), interpretability (topic coherence) and applicability (retrieval and classification) over 6 long-text and 8 short-text datasets from diverse domains.

Born in the city of Nagoya in 1970, he spent his teenage years devouring popular anime series of the time, including "Mobile Suit Zeta Gundam," the sequel in the well-known Gundam franchise that first aired in 1985, and "Dirty Pair," a sci-fi adventure featuring a sexy female duo working as "trouble consultants." This was the heyday of the VHS cassette, and Goto would spend his allowance renting anime tapes, many of which were made specifically for release on home video format to meet the period's surging demand for anime content. It wasn't a hobby he could openly share with his classmates, however. This was years before the otaku image underwent a makeover of sorts, thanks to the popularization of the fan culture and its global acceptance as a source of soft power. "Otaku of our generation were typically way down in the'school caste' system, and girls tended to look at us with disdain," he says, referring to the invisible hierarchy in the classroom determined by different status symbols.

The UK won't sit idly by while the US, Japan and China put self-driving vehicles on their roads. The country's government has announced an ambitious driverless public transport plan for 2021, including autonomous buses in Scotland and self-driving taxis in several of London's boroughs, with state funding to the tune of £25 million ($33 million). Scotland's first self-driving buses -- which could provide up to 10,000 journeys a week between Fife and Edinburgh across the Forth Bridge -- will get £4.35m Initially, the single-decker buses will require a human driver to be present at all times, with unmanned tests limited to the depot. The autonomous buses will be able to carry up to 42 passengers for the 14-mile journey and will operate every 20 minutes, according to the BBC.

Siri doesn't appear to think highly of Donald Trump. At least the virtual assistant didn't on Thanksgiving, when asking about the president showed an image of a penis instead. The error seems to be the result of someone editing Mr Trump's Wikipedia page and inserting the image. That meant it pulled through when Siri accessed the article to show more information – showing the explicit picture to the vast number of people who use the voice assistant on their iPhones. Uber has halted testing of driverless vehicles after a woman was killed by one of their cars in Tempe, Arizona.