LONDON – For many U.S. allies, Secretary of Defense Jim Mattis is the last of the Trump administration's so-called grown-ups in the room. So at Asia's main annual security forum he got a warm reception for his firm defense of the rules-based order the U.S. helped to build after World War II. Increasingly, though, Mattis' reassurance is not enough. The U.S. -- as much as China -- is seen as a threat to that system, undermining the very solutions the retired Marine Corps general offered to counter Beijing's rule breaking in the South China Sea. On Sunday, tiny Singapore, one of the United States' most like-minded partners in the region, drew a direct equivalence between the U.S. and China.

An action plan and ethical framework are critical to ensuring that people are educated and upskilled on artificial intelligence (AI) technologies. This is according to New Zealand Minister for Government Digital Services and Broadcasting, Communications and Digital Media Clare Curran. Curran spoke about the issue at the launch of the Artificial Intelligence: Shaping a Future New Zealand report, which outlines the opportunities and challenges for New Zealand in adopting AI. "An ethical framework will give people the tools to participate in conversations about AI and its implications in our society and economy," Curran said. "As a first step and because of the importance of ethics and governance issues around AI, I will be formalising the government's relationship with Otago University's NZ Law Foundation Centre for Law and Policy in Emerging Technologies. "There are economic opportunities but also some pressing risks and ethical challenges with AI, and New Zealand is lagging behind comparable countries in its work in these areas.

Anthony Seldon is one of Britain's leading educationalists and social commentators. He has served as a close adviser to former leaders, including Tony Blair and David Cameron. In recent years, he has turned his attention to the ongoing impact of new technologies, in particular, artificial intelligence (AI), on education and on society, writes Kyran Fitzgerald. With Oladimeji Abidoye, Mr Seldon recently published The Fourth Education Revolution: Will Artificial intelligence Liberate or Infantilise Humanity? He does not pull his punches, warning that we may be "sleepwalking into the biggest potential disaster of modern times". Despite these tides of change, the education sector has been slow to respond.

Proportional representation (PR) is often discussed in voting settings as a major desideratum. For the past century or so, it is common both in practice and in the academic literature to jump to single transferable vote (STV) as the solution for achieving PR. Some of the most prominent electoral reform movements around the globe are pushing for the adoption of STV. It has been termed a major open problem to design a voting rule that satisfies the same PR properties as STV and better monotonicity properties. In this paper, we first present a taxonomy of proportional representation axioms for general weak order preferences, some of which generalise and strengthen previously introduced concepts. We then present a rule called Expanding Approvals Rule (EAR) that satisfies properties stronger than the central PR axiom satisfied by STV, can handle indifferences in a convenient and computationally efficient manner, and also satisfies better candidate monotonicity properties. In view of this, our proposed rule seems to be a compelling solution for achieving proportional representation in voting settings.

Model-based clustering is a popular approach for clustering multivariate data which has seen applications in numerous fields. Nowadays, high-dimensional data are more and more common and the model-based clustering approach has adapted to deal with the increasing dimensionality. In particular, the development of variable selection techniques has received a lot of attention and research effort in recent years. Even for small size problems, variable selection has been advocated to facilitate the interpretation of the clustering results. This review provides a summary of the methods developed for variable selection in model-based clustering. Existing R packages implementing the different methods are indicated and illustrated in application to two data analysis examples.

This paper uses the relationship between graph conductance and spectral clustering to study (i) the failures of spectral clustering and (ii) the benefits of regularization. The explanation is simple. Sparse and stochastic graphs create a lot of small trees that are connected to the core of the graph by only one edge. Graph conductance is sensitive to these noisy `dangling sets'. Spectral clustering inherits this sensitivity. The second part of the paper starts from a previously proposed form of regularized spectral clustering and shows that it is related to the graph conductance on a `regularized graph'. We call the conductance on the regularized graph CoreCut. Based upon previous arguments that relate graph conductance to spectral clustering (e.g. Cheeger inequality), minimizing CoreCut relaxes to regularized spectral clustering. Simple inspection of CoreCut reveals why it is less sensitive to small cuts in the graph. Together, these results show that unbalanced partitions from spectral clustering can be understood as overfitting to noise in the periphery of a sparse and stochastic graph. Regularization fixes this overfitting. In addition to this statistical benefit, these results also demonstrate how regularization can improve the computational speed of spectral clustering. We provide simulations and data examples to illustrate these results.

In many applications, the interdependencies among a set of $N$ time series $\{ x_{nk}, k>0 \}_{n=1}^{N}$ are well captured by a graph or network $G$. The network itself may change over time as well (i.e., as $G_k$). We expect the network changes to be at a much slower rate than that of the time series. This paper introduces eigennetworks, networks that are building blocks to compose the actual networks $G_k$ capturing the dependencies among the time series. These eigennetworks can be estimated by first learning the time series of graphs $G_k$ from the data, followed by a Principal Network Analysis procedure. Algorithms for learning both the original time series of graphs and the eigennetworks are presented and discussed. Experiments on simulated and real time series data demonstrate the performance of the learning and the interpretation of the eigennetworks.

Solving inverse problems continues to be a challenge in a wide array of applications ranging from deblurring, image inpainting, source separation etc. Most existing techniques solve such inverse problems by either explicitly or implicitly finding the inverse of the model. The former class of techniques require explicit knowledge of the measurement process which can be unrealistic, and rely on strong analytical regularizers to constrain the solution space, which often do not generalize well. The latter approaches have had remarkable success in part due to deep learning, but require a large collection of source-observation pairs, which can be prohibitively expensive. In this paper, we propose an unsupervised technique to solve inverse problems with generative adversarial networks (GANs). Using a pre-trained GAN in the space of source signals, we show that one can reliably recover solutions to under determined problems in a `blind' fashion, i.e., without knowledge of the measurement process. We solve this by making successive estimates on the model and the solution in an iterative fashion. We show promising results in three challenging applications -- blind source separation, image deblurring, and recovering an image from its edge map, and perform better than several baselines.

Summary: A new artificial intelligence technique could speed up complex physics simulations and help create multilayered nanoparticles, researchers say. A new technique developed by MIT physicists could someday provide a way to custom-design multilayered nanoparticles with desired properties, potentially for use in displays, cloaking systems, or biomedical devices. It may also help physicists tackle a variety of thorny research problems, in ways that could in some cases be orders of magnitude faster than existing methods. The innovation uses computational neural networks, a form of artificial intelligence, to "learn" how a nanoparticle's structure affects its behavior, in this case the way it scatters different colors of light, based on thousands of training examples. Then, having learned the relationship, the program can essentially be run backward to design a particle with a desired set of light-scattering properties -- a process called inverse design.

Three crew members from the International Space Station (ISS) have arrived safely back on Earth after a mission of more than five months. A Soyuz capsule carrying Russian Anton Shkaplerov, American Scott Tingle and Japan's Norishige Kanai floated down under a red-and-white parachute for a landing on the steppes of Kazakhstan. Footage from the Russian space agency Roscosmos showed recovery helicopters circling as the capsule touched down at 18:39 local time (12:39 GMT) on Sunday, sending up a cloud of dust. Anton Shkaplerov, who was the first to be lifted and carried from the capsule, told the camera crew: "We are a bit tired but happy with what we have accomplished and happy to be back on Earth. We are glad the weather is sunny."