The ability to build a model on a source task and subsequently adapt such model on a new target task is a pervasive need in many astronomical applications. The problem is generally known as transfer learning in machine learning, where domain adaptation is a popular scenario. An example is to build a predictive model on spectroscopic data to identify Supernovae IA, while subsequently trying to adapt such model on photometric data. In this paper we propose a new general approach to domain adaptation that does not rely on the proximity of source and target distributions. Instead we simply assume a strong similarity in model complexity across domains, and use active learning to mitigate the dependency on source examples. Our work leads to a new formulation for the likelihood as a function of empirical error using a theoretical learning bound; the result is a novel mapping from generalization error to a likelihood estimation. Results using two real astronomical problems, Supernova Ia classification and identification of Mars landforms, show two main advantages with our approach: increased accuracy performance and substantial savings in computational cost.

Traffic speed data imputation is a fundamental challenge for data-driven transport analysis. In recent years, with the ubiquity of GPS-enabled devices and the widespread use of crowdsourcing alternatives for the collection of traffic data, transportation professionals increasingly look to such user-generated data for many analysis, planning, and decision support applications. However, due to the mechanics of the data collection process, crowdsourced traffic data such as probe-vehicle data is highly prone to missing observations, making accurate imputation crucial for the success of any application that makes use of that type of data. In this article, we propose the use of multi-output Gaussian processes (GPs) to model the complex spatial and temporal patterns in crowdsourced traffic data. While the Bayesian nonparametric formalism of GPs allows us to model observation uncertainty, the multi-output extension based on convolution processes effectively enables us to capture complex spatial dependencies between nearby road segments. Using 6 months of crowdsourced traffic speed data or "probe vehicle data" for several locations in Copenhagen, the proposed approach is empirically shown to significantly outperform popular state-of-the-art imputation methods.

Time series anomaly detection is usually formulated as finding outlier data points relative to some usual data, which is also an important problem in industry and academia. To ensure systems working stably, internet companies, banks and other companies need to monitor time series, which is called KPI (Key Performance Indicators), such as CPU used, number of orders, number of online users and so on. However, millions of time series have several shapes (e.g. seasonal KPIs, KPIs of timed tasks and KPIs of CPU used), so that it is very difficult to use a simple statistical model to detect anomaly for all kinds of time series. Although some anomaly detectors have developed many years and some supervised models are also available in this field, we find many methods have their own disadvantages. In this paper, we present our system, which is based on deep feedforward neural network and detect anomaly points of time series. The main difference between our system and other systems based on supervised models is that we do not need feature engineering of time series to train deep feedforward neural network in our system, which is essentially an end-to-end system.

Many applications of machine learning involve the analysis of large data frames-matrices collecting heterogeneous measurements (binary, numerical, counts, etc.) across samples-with missing values. Low-rank models, as studied by Udell et al. [30], are popular in this framework for tasks such as visualization, clustering and missing value imputation. Yet, available methods with statistical guarantees and efficient optimization do not allow explicit modeling of main additive effects such as row and column, or covariate effects. In this paper, we introduce a low-rank interaction and sparse additive effects (LORIS) model which combines matrix regression on a dictionary and low-rank design, to estimate main effects and interactions simultaneously. We provide statistical guarantees in the form of upper bounds on the estimation error of both components. Then, we introduce a mixed coordinate gradient descent (MCGD) method which provably converges sub-linearly to an optimal solution and is computationally efficient for large scale data sets. We show on simulated and survey data that the method has a clear advantage over current practices, which consist in dealing separately with additive effects in a preprocessing step.

On June 6, 2015, the U.S. Department of Justice brought the first-ever online market-place prosecution against a price-fixing cartel. One of the special features of the case was that prices were set by algorithms. Topkins and his competitors designed and shared dynamic pricing algorithms that were programmed to act in conformity with their agreement to set coordinated prices for posters sold online. They were found to engage in an illegal cartel. Following the case, the Assistant Attorney General stated that "[w]e will not tolerate anticompetitive conduct, [even if] it occurs...over the Internet using complex pricing algorithms."

Google has been forced to abandon its specialist Chinese search engine that censors results in line with the strict government, reports have claimed. The firm is believed to have shut down an internal data analysis system which was being used to develop the search engine, known as Dragonfly. According to a report from The Intercept, this has'effectively ended' the entire project. Members of Google's privacy team raised concerns about the project back in August and it is now extremely unlikely the search engine can be built without the system, according to sources close to the project. Google has been forced to abandon its plan to launch a specialist Chinese search engine that censors results in line with the strict government.

Japanese startup Groove X, founded by an alumni of SoftBank Group Corp's robotics unit, unveiled its first creation on Tuesday - a companion robot designed to make users happy. The Lovot, an amalgam of'love' and'robot', cannot help with the housework but it will'draw out your ability to love,' Groove X founder and CEO Kaname Hayashi told reporters at the launch in Tokyo. Using artificial intelligence (AI) to interact with its surroundings, the wheeled machine resembles a penguin with cartoonish human eyes, has interchangeable outfits and communicates in squeaks. Groove X's Lovot robots are displayed at their demonstration during the launch event in Tokyo. Using artificial intelligence (AI) to interact with its surroundings, the wheeled machine resembles a penguin with cartoonish human eyes, has interchangeable outfits and communicates in squeaks.

For something that ends with something so pleasant, shoe shopping can sometimes seem like the worst kind of work: concern that a shop won't have your size, asking to find out if they do and try it on, only to discover that size doesn't fit and being forced to trudge back ashamed and ask for a different size, before being forced to wait all over again as you try and check out. Nike, it turns out, wants to put a stop to that kind of shopping just as much as you do. And with its latest additions to its app, it appears to have succeeded. The company is just one of a range of firms betting that the future of retail looks a little like its past, and that traditional shops aren't being killed by technology but enhanced by it. The company's new update – known as Nike App At Retail, and newly launched at its London Store right on Oxford Circus – allows you to shop right from the app, choosing your size and style and having it checked out seamlessly.

Dec 19, 2018 (China Knowledge) - China's biggest search engine Baidu has confirmed plans to restructure, in order to solidify its foundation in AI and raise its shares in cloud computing. This announcement was made through an internal letter written by CEO of the company, Robin Li. The new restructuring will be based on company's ABC corporate strategy. In the ABC corporate strategy Baidu will focus on the development of Artificial Intelligence (AI), Big Data and Cloud Computing. The company has plans to upgrade its former AI and cloud computing unit into a business group under the same name.

Stagnation in the proportion of women in the workplace and women's declining representation in politics, coupled with greater inequality in access to health and education, offset improvements in wage equality and the number of women in professional positions, leaving the global gender gap only slightly reduced in 2018. This is according to the Forum's Global Gender Gap Report 2018, published today. According to the report, the world has closed 68% of its gender gap, as measured across four key pillars: economic opportunity; political empowerment; educational attainment; and health and survival. While only a marginal improvement on 2017, the move is nonetheless welcome as 2017 was the first year since the report was first published in 2006 that the gap between men and women widened. At the current rate of change, the data suggest that it will take 108 years to close the overall gender gap and 202 years to bring about parity in the workplace.