Mr. Smith Goes to Washington 1939 TCM Tue. 7 p.m. Mean Streets 1973 Cinemax Sun. 6 a.m. Batman Begins 2005 AMC Sun. Throw Momma From the Train 1987 EPIX Sun. Die Hard 1988 IFC Sun. I Know What You Did Last Summer 1997 Starz Tue. Gone in 60 Seconds 2000 CMT Wed. 8 p.m., Thur. Total Recall 1990 Encore Thur. 2 a.m. A Fish Called Wanda 1988 Encore Thur. 2 p.m., 9 p.m. The World Is Not Enough 1999 EPIX Sat. 4 p.m. Look Who's Talking 1989 OVA Sun. Die Hard With a Vengeance 1995 IFC Thur. Oil-platform workers, including an estranged couple, and a Navy SEAL make a startling deep-sea discovery. A clueless politician falls in love with a waitress whose erratic behavior is caused by a nail stuck in her head. After glimpsing his future, an ambitious politician battles the agents of Fate itself to be with the woman he loves. To help a friend, a suburban baby sitter drives into downtown Chicago with her two charges and a neighbor. Two teenage baby sitters and a group of children spend a wild night ...

England fans know the drill all too well - the national team heads into a major international football tournament having qualified with a near-perfect record. Hopes are high, but then… well, you know what happens - lacklustre performances or penalty shoot-out heartbreak, followed by an early flight home. So what really determines the success or failure of a team going into a major international football tournament like the World Cup? Is it a side's quality (class) or its recent performances (form)? Reality Check has teamed up with the BBC's statistics department to try to answer one of the biggest debates in football - how much does form matter? To do this, we built a computer program that predicts football results by analysing ratings data.

The US just took back the title for the world's fastest supercomputer. On Friday, the US Department of Energy's Oak Ridge National Laboratory (ORNL) in Tennessee unveiled the'Summit' supercomputer that can deliver a peak performance of 200 petaflops, or about 200 quadrillion calculations per second. It managed to beat out the previous record holder that was China's Sunway TaihuLight supercomputer. Summit is 60% faster than the TaihuLight supercomputer, which could achieve a peak performance of 93 petaflops. The feat puts the US at the front of the top 500 supercomputers in the world -- the first time it has held such ranking since June 2013. Summit has been in development for several years now and is made up of thousands of chips.

At the Computing Conference 2018: Shanghai Summit on June 7, Alibaba Cloud introduced its ET Agricultural Brain that aims to lower pigs' death rate by 3% and allow each sow to raise three more piglets each year. According to its President, Simon Hu, ET Agricultural Brain can monitor each pig's daily activity, growth indicators and other health indexes using AI technologies such as visual recognition, voice recognition, and real-time environmental parameter monitoring. Hu said that active pigs will become favored over heavier ones: a pig that runs 200 km over the course of its life will be sold over a 100 kg one. Sharing his company's experiences using ET Agricultural Brain at the conference, Sichuan-based pig farming enterprise Tequ Group's Chairman Wang Degen said that Alibaba Cloud's technology and ecosystem integrates cutting edge interactive automation with hog farming. Other early adopters include the Shaanxi-based agricultural company Haisheng Group, which according to Alibaba Cloud's estimation, could save around USD 3.1M in annual operating costs by using the technology. ET Agricultural Brain has also been successfully used in the smart-city, transportation, industrial, and aviation sectors.

On Thursday, Google released a document entitled "Artificial Intelligence at Google: Our Principles," vowing to avoid Pentagon projects to develop AI weapons. However, they'll continue to work with the US military on a host of other projects, including AI projects, so long as they don't include surveillance that runs counter to human rights. "Google is in a spot at the moment," Wallis, editor-at-large for Digital Journal and author of more than a dozen books, told Loud & Clear hosts John Kiriakou and Nicole Roussell. "This is gold rush time for artificial intelligence; everybody wants a piece of it, and we're not talking about the sort of generic type of artificial intelligence -- we're talking about possibly thousands or millions of different kinds of artificial intelligence." In other words, the advent of artificially intelligent weapons is "inevitable," Wallis said.

On Thursday, Google released a document entitled "Artificial Intelligence at Google: Our Principles," vowing to avoid Pentagon projects to develop AI weapons. However, they'll continue to work with the US military on a host of other projects, including AI projects, so long as they don't include surveillance that runs counter to human rights. "Google is in a spot at the moment," Wallis, editor-at-large for Digital Journal and author of more than a dozen books, told Loud & Clear hosts John Kiriakou and Nicole Roussell. "This is gold rush time for artificial intelligence; everybody wants a piece of it, and we're not talking about the sort of generic type of artificial intelligence -- we're talking about possibly thousands or millions of different kinds of artificial intelligence." In other words, the advent of artificially intelligent weapons is "inevitable," Wallis said.

Hierarchical clustering is a class of algorithms that seeks to build a hierarchy of clusters. It has been the dominant approach to constructing embedded classification schemes since it outputs dendrograms, which capture the hierarchical relationship among members at all levels of granularity, simultaneously. Being greedy in the algorithmic sense, a hierarchical clustering partitions data at every step solely based on a similarity / dissimilarity measure. The clustering results oftentimes depend on not only the distribution of the underlying data, but also the choice of dissimilarity measure and the clustering algorithm. In this paper, we propose a method to incorporate prior domain knowledge about entity relationship into the hierarchical clustering. Specifically, we use a distance function in ultrametric space to encode the external ontological information. We show that popular linkage-based algorithms can faithfully recover the encoded structure. Similar to some regularized machine learning techniques, we add this distance as a penalty term to the original pairwise distance to regulate the final structure of the dendrogram. As a case study, we applied this method on real data in the building of a customer behavior based product taxonomy for an Amazon service, leveraging the information from a larger Amazon-wide browse structure. The method is useful when one wants to leverage the relational information from external sources, or the data used to generate the distance matrix is noisy and sparse. Our work falls in the category of semi-supervised or constrained clustering.

An imprecise region is referred to as a geographical area without a clearly-defined boundary in the literature. Previous clustering-based approaches exploit spatial information to find such regions. However, the prior studies suffer from the following two problems: the subjectivity in selecting clustering parameters and the inclusion of a large portion of the undesirable region (i.e., a large number of noise points). To overcome these problems, we present DIR-ST$^2$, a novel framework for delineating an imprecise region by iteratively performing density-based clustering, namely DBSCAN, along with not only spatio--textual information but also temporal information on social media. Specifically, we aim at finding a proper radius of a circle used in the iterative DBSCAN process by gradually reducing the radius for each iteration in which the temporal information acquired from all resulting clusters are leveraged. Then, we propose an efficient and automated algorithm delineating the imprecise region via hierarchical clustering. Experiment results show that by virtue of the significant noise reduction in the region, our DIR-ST$^2$ method outperforms the state-of-the-art approach employing one-class support vector machine in terms of the $\mathcal{F}_1$ score from comparison with precisely-defined regions regarded as a ground truth, and returns apparently better delineation of imprecise regions. The computational complexity of DIR-ST$^2$ is also analytically and numerically shown.

One of the biggest challenges in the research of generative adversarial networks (GANs) is assessing the quality of generated samples and detecting various levels of mode collapse. In this work, we construct a novel measure of performance of a GAN by comparing geometrical properties of the underlying data manifold and the generated one, which provides both qualitative and quantitative means for evaluation. Our algorithm can be applied to datasets of an arbitrary nature and is not limited to visual data. We test the obtained metric on various real-life models and datasets and demonstrate that our method provides new insights into properties of GANs.

A measure of relative importance of variables is often desired by researchers when the explanatory aspects of econometric methods are of interest. To this end, the author briefly reviews the limitations of conventional econometrics in constructing a reliable measure of variable importance. The author highlights the relative stature of explanatory and predictive analysis in economics and the emergence of fruitful collaborations between econometrics and computer science. Learning lessons from both, the author proposes a hybrid approach based on conventional econometrics and advanced machine learning (ML) algorithms, which are otherwise, used in predictive analytics. The purpose of this article is two-fold, to propose a hybrid approach to assess relative importance and demonstrate its applicability in addressing policy priority issues with an example of food inflation in India, followed by a broader aim to introduce the possibility of conflation of ML and conventional econometrics to an audience of researchers in economics and social sciences, in general.