Predicting interactions between structured entities lies at the core of numerous tasks such as drug regimen and new material design. In recent years, graph neural networks have become attractive. They represent structured entities as graphs and then extract features from each individual graph using graph convolution operations. However, these methods have some limitations: i) their networks only extract features from a fix-sized subgraph structure (i.e., a fix-sized receptive field) of each node, and ignore features in substructures of different sizes, and ii) features are extracted by considering each entity independently, which may not effectively reflect the interaction between two entities. To resolve these problems, we present MR-GNN, an end-to-end graph neural network with the following features: i) it uses a multi-resolution based architecture to extract node features from different neighborhoods of each node, and, ii) it uses dual graph-state long short-term memory networks (L-STMs) to summarize local features of each graph and extracts the interaction features between pairwise graphs. Experiments conducted on real-world datasets show that MR-GNN improves the prediction of state-of-the-art methods.

In this work, we addressed the issue of combining linear classifiers using their score functions. The value of the scoring function depends on the distance from the decision boundary. Two score functions have been tested and four different combination strategies were investigated. During the experimental study, the proposed approach was applied to the heterogeneous ensemble and it was compared to two reference methods -- majority voting and model averaging respectively. The comparison was made in terms of seven different quality criteria. The result shows that combination strategies based on simple average, and trimmed average are the best combination strategies of the geometrical combination.

We propose to learn a hierarchical prior in the context of variational autoencoders to avoid the over-regularisation resulting from a standard normal prior distribution. To incentivise an informative latent representation of the data by learning a rich hierarchical prior, we formulate the objective function as the Lagrangian of a constrained-optimisation problem and propose an optimisation algorithm inspired by Taming VAEs. We introduce a graph-based interpolation method, which shows that the topology of the learned latent representation corresponds to the topology of the data manifold---and present several examples, where desired properties of latent representation such as smoothness and simple explanatory factors are learned by the prior. Furthermore, we validate our approach on standard datasets, obtaining state-of-the-art test log-likelihoods.

Facial recognition cameras prevent crime, protect the public and do not breach the privacy of innocent people whose images are captured, a police force has argued. Ed Bridges, an office worker from Cardiff, claims South Wales police violated his privacy and data protection rights by using facial recognition technology on him. But Jeremy Johnson QC compared automated facial recognition (AFR) to the use of DNA to solve crimes and said it would have had little impact on Bridges. Johnson, representing the police, said: "AFR is a further technology that potentially has great utility for the prevention of crime, the apprehension of offenders and the protection of the public." The technology maps faces in a crowd and then compares them with a watch list of images, which can include suspects, missing people and persons of interest to the police.

A shape-shifting drone can transform into a car once it touches down. The drone, called FSTAR, can move through a variety of surfaces and environments, making it a potentially helpful tool in search and rescue missions. FSTAR has a wheel and a propeller on each of its four legs. The prototype is about 35 centimeters long and 25 centimeters wide. During operation, a human pilot uses a controller to drive FSTAR and change its configurations.

Digit-diallers in Japan will be jumping for joy as the country plans 10 billion new phone numbers – that's 80 per person, which is probably enough. Researchers have used an X-ray laser to create the loudest possible underwater sound, at 270 decibels. Any louder, and the water would boil. In a battle of the blocks, Minecraft has become the best-selling video game of all time, beating Tetris with 176 million copies sold. The cryptocurrency hit a recent high of $8000, but, honestly, who knows what the price will be by the time you're reading this.

We are in the midst of an exciting time for data policy in the United States. There are few points in history when government's policymakers have been so enthused by the topic of data – and in a promising way. As the Data Coalition's new CEO, I'm excited to lead our organizations and members into this new era. Whether you come from the open data, evidence, science, evaluation, statistics, or privacy community, there are many encouraging activities underway inside government to make data more accessible and useful. For those interested in an effective and efficient government that actually meets the needs of the American public, accessibility of information about policies and programs is essential.

Python has conquered the machine learning and AI world. Here's an interesting article from Analytics India Magazine about why Python is on top. According to the Stack Overflow Survey 2018, Python is the most wanted language for the second year in a row, which means it is the language that developers who do not yet use it most often say they want to learn. It is also claimed to be the fastest-growing major programming language. Developers and pioneers around the globe are implementing this language for machine learning projects.

How will AI change the field of marketing? How can your prepare for this? Join IMD business school's signature program Orchestrating Winning Performance to get the full picture on AI & marketing and 40 other critical business topics for 2019.

Inland Empire Health Plan in Rancho Cucamonga, California, is the largest not-for-profit Medi-Cal and Medicare health plan in the Inland Empire, a metropolitan area in Southern California. Comprehensive medication reviews, a significant utilizer of trained clinical pharmacist resources, were not focused on plan members most in need, and the review process was manual and time-consuming. Moreover, it was unclear if member outcomes were improving because of this program. Preveon is a specialty pharmacy focused on chronic disease management. Inland Empire Health Plan has outsourced its medication review process to Preveon for its MyMeds Program, and as such, purchased Surveyor Health licenses and allocated them to Preveon.