Recent advances in representation learning on graphs, mainly leveraging graph convolutional networks, have brought a substantial improvement on many graph-based benchmark tasks. While novel approaches to learning node embeddings are highly suitable for node classification and link prediction, their application to graph classification (predicting a single label for the entire graph) remains mostly rudimentary, typically using a single global pooling step to aggregate node features or a hand-designed, fixed heuristic for hierarchical coarsening of the graph structure. An important step towards ameliorating this is differentiable graph coarsening---the ability to reduce the size of the graph in an adaptive, data-dependent manner within a graph neural network pipeline, analogous to image downsampling within CNNs. However, the previous prominent approach to pooling has quadratic memory requirements during training and is therefore not scalable to large graphs. Here we combine several recent advances in graph neural network design to demonstrate that competitive hierarchical graph classification results are possible without sacrificing sparsity. Our results are verified on several established graph classification benchmarks, and highlight an important direction for future research in graph-based neural networks.

We provide sharp worst-case evaluation complexity bounds for nonconvex minimization problems with general inexpensive constraints, i.e.\ problems where the cost of evaluating/enforcing of the (possibly nonconvex or even disconnected) constraints, if any, is negligible compared to that of evaluating the objective function. These bounds unify, extend or improve all known upper and lower complexity bounds for unconstrained and convexly-constrained problems. It is shown that, given an accuracy level $\epsilon$, a degree of highest available Lipschitz continuous derivatives $p$ and a desired optimality order $q$ between one and $p$, a conceptual regularization algorithm requires no more than $O(\epsilon^{-\frac{p+1}{p-q+1}})$ evaluations of the objective function and its derivatives to compute a suitably approximate $q$-th order minimizer. With an appropriate choice of the regularization, a similar result also holds if the $p$-th derivative is merely H\"older rather than Lipschitz continuous. We provide an example that shows that the above complexity bound is sharp for unconstrained and a wide class of constrained problems, we also give reasons for the optimality of regularization methods from a worst-case complexity point of view, within a large class of algorithms that use the same derivative information.

Though insect-sized flying robots have been around for a while, none had been able to take untethered fight until now. Engineers at the University of Washington have revealed the RoboFly had taken its first untethered flaps, earlier this year, marking the first time a wireless flying robotic insect has flown. Now the man behind the project has revealed he hopes to have fully autonomous swarms roaming the skies within five years. RoboFly is only slightly heavier than a toothpick and is powered by an onboard circuit that converts the laser energy into enough electricity to operate its wings. Previously, the electronics the insects carried to power and control their wings were too heavy for the robots to fly with, meaning they had to remain connected to a wire.

The development of so-called neurostimulators may lead to dystopian scenarios whereby hackers create false memories and implant them in people's brains, researchers have warned. The human brain is vulnerable to manipulation through implantable medical devices used to treat things like Parkinson's, according to a practical and theoretical review of this and other scenarios undertaken by the University of Oxford Functional Neurosurgery Group and Russian cyber security firm Kaspersky. Within a decade, technology will also have progressed to the point that commercial memory boosting implants will be available to buy, according to the researchers, while 20 years from now could see a time when it will the technology will be advanced enough to allow for "extensive control over memories." The development of these technologies will have a number of healthcare benefits and will open up the possibility of new bio-connected technologies like increased brain capacity, however it also holds the potential for exploitation and abuse. "New threats resulting from this could include the mass manipulation of groups through implanted or erased memories of political events or conflicts; while'repurposed' cyberthreats could target new opportunities for cyber-espionage or the theft, deletion of, or'locking' of memories (for example, in return for a ransom)," the researchers wrote in their report'The Memory Market: Preparing for a future where cyberthreats target your past'.

Younger people prefer assistance from female voices, neuroscience research has revealed. A major study of consumers' subconscious reactions to digitised voices has revealed there is a substantial bias towards female voices among people under the age of 35. The research, which used brain imaging technology to explore the neurological response to male and female voice assistants, discovered respondents of both genders within a younger age group found a female voice both more approachable and more compelling than its male equivalent. In contrast, while those over 35 also found the female voice more approachable, they found the male voice to be more compelling. Analysing the subconscious brain responses of 105 subjects aged between 18 and 65, the study discovered that subjects of all ages and genders found female voices more approachable, with the "approach" response 32 per cent higher for female voices.

Twitter has apologised after the phrase "Kill all Jews" appeared to a number of users. The words came up as part of a trending topic on the site's homepage, which meant that people were shown the violently antisemitic messages when they logged in. The phrase had been posted because of an attack on a synagogue in New York, where the words were sprayed onto a wall in just the latest of a series of high-profile attacks on Jewish people. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

Since the start of the First Industrial Revolution, manufacturing has been the force pushing industrial and societal transformation forward. Today, we're in the midst of the Fourth Industrial Revolution, as a new generation of sophisticated technologies is transforming manufacturing into a highly connected, intelligent, and ultimately, more productive industry. The manpowered shop floor of the past is being replaced by smart manufacturing facilities where tech-savvy workers, aided by intelligent robots, are creating the products and services of the future. As we approach 2019, we're looking ahead to the trends that will define intelligent manufacturing, as well as help empower clients to better evaluate and manage operations, build innovative products and services, and grow their manufacturing businesses. These trends are detailed in our new 2019 Manufacturing Trends Report.

Researchers created a machine learning software that analyzes virus' genetic information to predict which groups of animals the virus will likely spread to, according to a paper published Nov. 1 in Science. For the study, researchers at the University of Glasgow in the U.K. collected epidemiological and genetic data on several hundred viruses with known animal hosts that can spread to humans. Using machine learning, researchers created a computer model to predict which animal groups would most likely host a virus, based on its RNA genome. The model can predict one of 11 likely animal groups, such as rodents or primates, but cannot identify a specific species as the likely virus carrier. "We would love to know the species," said Daniel Streicker, PhD, a disease ecologist at the University of Glasgow and lead study author.

Pascal Weinberger in conversation with his team at Telefonica's "moonshots" division Alpha, where he heads AI research and development. Flying cars, augmented reality glasses and contact lenses that can detect diabetes: They're all innovations born out of Google X, the skunkworks division of Alphabet. Three years ago Spanish telco giant Telefónica established Alpha, a lab in Barcelona staffed by around 100 people, working in stealth on innovative technology that holds the promise of a potential new revenue streams. The person running all things AI at the lab is Pascal Weinberger, 21. Weinberger is originally from Germany and like many other computer programmers is self-taught.

The warning by Lars Klingbeil, general secretary of the Social Democratic Party, comes less than two weeks before Chancellor Angela Merkel's squabbling coalition is due to sign off on the blueprint. An initial draft released earlier this year fell short of expectations, Klingbeil wrote in a commentary for the Tagesspiegel newspaper: "We really need a breakout. We have to stop taking it easy." Policy makers in Europe's largest economy have been late to realize that its industrial export model is vulnerable at a time of rapid digitalization and growing trade frictions. Car makers such as Volkswagen (VOWG_p.DE) or Daimler are vulnerable to competition from asset-light companies, such as ride-hailing platform Uber, that use artificial intelligence - essentially computers that can be trained to solve problems - to crunch data and run digital services.