A recent theoretical analysis shows the equivalence between non-negative matrix factorization (NMF) and spectral clustering based approach to subspace clustering. As NMF and many of its variants are essentially linear, we introduce a nonlinear NMF with explicit orthogonality and derive general kernel-based orthogonal multiplicative update rules to solve the subspace clustering problem. In nonlinear orthogonal NMF framework, we propose two subspace clustering algorithms, named kernel-based non-negative subspace clustering KNSC-Ncut and KNSC-Rcut and establish their connection with spectral normalized cut and ratio cut clustering. We further extend the nonlinear orthogonal NMF framework and introduce a graph regularization to obtain a factorization that respects a local geometric structure of the data after the nonlinear mapping. The proposed NMF-based approach to subspace clustering takes into account the nonlinear nature of the manifold, as well as its intrinsic local geometry, which considerably improves the clustering performance when compared to the several recently proposed state-of-the-art methods.

We consider the problem of online subspace tracking of a partially observed high-dimensional data stream corrupted by noise, where we assume that the data lie in a low-dimensional linear subspace. This problem is cast as an online low-rank tensor completion problem. We propose a novel online tensor subspace tracking algorithm based on the CANDECOMP/PARAFAC (CP) decomposition, dubbed OnLine Low-rank Subspace tracking by TEnsor CP Decomposition (OLSTEC). The proposed algorithm especially addresses the case in which the subspace of interest is dynamically time-varying. To this end, we build up our proposed algorithm exploiting the recursive least squares (RLS), which is the second-order gradient algorithm. Numerical evaluations on synthetic datasets and real-world datasets such as communication network traffic, environmental data, and surveillance videos, show that the proposed OLSTEC algorithm outperforms state-of-the-art online algorithms in terms of the convergence rate per iteration.

Latent features learned by deep learning approaches have proven to be a powerful tool for machine learning. They serve as a data abstraction that makes learning easier by capturing regularities in data explicitly. Their benefits motivated their adaptation to relational learning context. In our previous work, we introduce an approach that learns relational latent features by means of clustering instances and their relations. The major drawback of latent representations is that they are often black-box and difficult to interpret. This work addresses these issues and shows that (1) latent features created by clustering are interpretable and capture interesting properties of data; (2) they identify local regions of instances that match well with the label, which partially explains their benefit; and (3) although the number of latent features generated by this approach is large, often many of them are highly redundant and can be removed without hurting performance much.

The European Robotics League (ERL) announced the winners of ERL Emergency Robots 2017 major tournament, during the awards ceremony held on Saturday, 23rd September at Giardini Pro Patria, in Piombino, Italy. In addition to the Competition Awards, Marta Palau Franco from Bristol Robotics Laboratory and ERL Emergency project manager introduced the referees' special awards. "Behind a multi-domain competition there is always a large technical committee, I feel privileged to have worked with such an amazing team of volunteer referees, technical assistants and safety pilots and divers. We were delighted to give these awards to recognise teams' effort, fair play and hard work. The experience of participating in this robotics competition will prove beneficial for team members to develop further their professional career", said Marta Palau Franco.

The European Robotics League (ERL) announced the winners of ERL Emergency Robots 2017 major tournament, during the awards ceremony held on Saturday, 23rd September at Giardini Pro Patria, in Piombino, Italy. The ERL Emergency Robots 2017 competition consisted of four scenarios, inspired by the nuclear accident of Fukushima (Japan, 2011) and designed specifically for multi-domain human-robot teams. The first scenario is The Grand Challenge made up of three domains – sea, air, land, and the other three scenarios are made of only two domains. The Awards, given for each scenario to the best performing teams, were introduced by Alan Winfield from Bristol Robotics Laboratory and ERL Emergency Coordinator. "The energy, enthusiasm and spirit of cooperation among the teams competing in ERL Emergency was amazing. We witnessed not only great performances from the teams and their robots, but also the drama and excitement of last minute field repairs and workarounds to the robots", said Alan Winfield.

In Switzerland, executives from the three partner companies showcased the drone in action on Thursday, with a woman enacting the scene of loading up the drone with a bag of coffee that was flown several kilometers and landed smoothly on the rooftop of a Mercedes-Benz van. After high-fives at the successful flight, the coffee was then brewed up at a coffee cart at the ready and served for the several dozen attendees.

Are you a Machine Learning Scientist and do you know of the state-of-the-art tooling in capturing content and translating human annotations to machine models? Are you familiar with deep learning algorithms and solutions? We have the right opportunity for you! In line with the Elsevier corporate strategy of greater content volume, types and sophistication, the services that Elsevier provides are becoming increasingly dependent on Smart Content. We are therefore looking for a Machine Learning Scientist who can focus on designing and creating systems that enable machine learning in the context of article submission systems and other systems where authors or other human agents can enter metadata and other structured data to publications.

On Thursday 7 September, the Director of UNICRI, Cindy Smith, and the Ambassador of the Kingdom of the Netherlands to the International Organizations, Johan van der Werff, signed the Host Country Agreement for the establishment of the first United Nations Centre for Artificial Intelligence and Robotics, in The Hague, The Netherlands. The benefits of AI and robotics will be of paramount importance for society in the years to come. Critical areas such as health, education, energy, economic inclusion, social welfare, the environment, as well as crime prevention, security, stability and justice, will benefit from the progress being made. However, this growth will not be costless if societies will not be prepared to take up the gauntlet to address relevant challenges, for the greater good. Many of these challenges also present opportunities that can be developed if the implications involved by this technological revolution are addressed from the very beginning.

In an interview at Singularity University's Global Summit in San Francisco, Anita Schjøll Brede talked about how artificial intelligence can help make scientific research accessible to anyone working on a complex problem. Anita Schjøll Brede is the CEO and co-founder of Iris AI, a startup that's building an artificially intelligent research assistant, which was recently named one of the most innovative AI startups of 2017 by Fast Company. Schjøll Brede is also faculty at Singularity University Denmark and a 2015 alumni of the Global Solutions Program. "Ultimately, we're building an AI that can read, understand, and connect the dots," Schjøll Brede said. "But zooming that back into today, we're building a tool for R&D, research institutions, and entrepreneurs who have big hairy problems to solve and need to apply research and science to solve them. We're semi-automating the process of mapping out what you should read to solve the problem or to see what research you need to do to solve the problem."

A drone that can switch between flying and rolling could soon be exploring underground mines without the aid of a human pilot. In open air, drones can navigate autonomously using GPS, but these satellite signals don't penetrate deep underground, meaning robot spelunkers require human pilots. Ahmed AlNomany and his colleagues at Swedish company Inkonova are working on an alternative. "It's complicated because we are trying to invent another way of positioning using bits and pieces of technologies," says AlNomany. Having a view of its surroundings is the first step.