We developed a convolution neural network (CNN) on semi-regular triangulated meshes whose vertices have 6 neighbours. The key blocks of the proposed CNN, including convolution and down-sampling, are directly defined in a vertex domain. By exploiting the ordering property of semi-regular meshes, the convolution is defined on a vertex domain with strong motivation from the spatial definition of classic convolution. Moreover, the down-sampling of a semi-regular mesh embedded in a 3D Euclidean space can achieve a down-sampling rate of 4, 16, 64, etc. We demonstrated the use of this vertex-based graph CNN for the classification of mild cognitive impairment (MCI) and Alzheimer's disease (AD) based on 3169 MRI scans of the Alzheimer's Disease Neuroimaging Initiative (ADNI). We compared the performance of the vertex-based graph CNN with that of the spectral graph CNN.

Apposite Capital invests in Mirada Medical London, UK – 8th April 2019. Apposite Capital, the private equity firm with an exclusive focus on healthcare, today announces that it has invested £6m in preferred equity in Mirada Medical, a world leading medical imaging software company. The investment will support Mirada's development of artificial intelligence (#AI) based software for increasing the speed and accuracy of #cancer treatment planning applications, while delivering a more personalised level of care to patients. Apposite has invested alongside Mirada's founding investor, AlbionVC, and other private investors. Skymind raises $11.5M Skymind (*), a Y Combinator-incubated AI platform that aims to make deep learning more accessible to enterprises, today announced that it has raised an $11.5 million Series A round led by TransLink Capital, with participation from ServiceNow, Sumitomo's Presidio Ventures, UpHonest Capital and GovTech Fund.

How to contact us Freddy White 44 7968 565401 freddy@ai-med.io

Channing Powell, the creator of the hit horror television series'The Walking Dead', is not someone who is easily spooked. But Powell is scared, 'terrified actually' of what big tech might be up to. And critics were too after watching her spine-chilling new series, 'The Feed', premiere in Cannes this week. The Amazon show is set in the near future when we can share emotions, thoughts and what we see with our eyes on a social network embedded in our brains. If that sounds as far fetched as the post-apocalyptic zombies of'The Walking Dead', Powell has news for you.

A dexterous robot arm that can can automatically feed people forkfuls of food has been developed by researchers in the US. Experts studied how real people use forks to feed each other in order to teach the robot the best way to go about its task. The arm automatically adjusts both the force it uses and the angle at which it spears items to best pick up and deliver mouthfuls of food - regardless of size or texture. A dexterous robot arm that can can automatically feed people forkfuls of food has been developed by researchers in the US. 'Being dependent on a caregiver to feed every bite, every day, takes away a person's sense of independence,' said roboticist Siddhartha Srinivasa. 'Our goal with this project is to give people a bit more control over their lives.'

The music management system was once seen as the future of computing: it was used to control the iPod, and was home to the iTunes Store, both of which helped to revolutionise the way people buy and listen to music. But with time it has become bloated with additional features – from watching films to managing devices like phones – and its performance has dropped. That has led to it becoming largely despised within the tech community. We'll tell you what's true. You can form your own view.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. The 2019 IEEE International Conference on Soft Robotics (RoboSoft) takes place in Seoul, South Korea, next week, and the organizers put together this preview video stuffed full of--what else?--soft robots. Single-stream recycling is currently an extremely labor intensive process due to the need for manual object sorting.

A collaboration between researchers from China's Beihang University and Microsoft Research Asia has produced TableBank, a new image-based dataset for table detection and recognition built with novel weak supervision from Word and Latex documents on the Internet. Researchers built several strong baselines using SOTA models with deep neural networks, which will enable deployment of more deep learning methods to table detection and recognition tasks. TableBank has been open-sourced on Github. "Existing research for image-based table detection and recognition usually fine-tunes pre-trained models on out-of-domain data with a few thousands human labeled examples, which is difficult to generalize on real world applications. With TableBank that contains 417K high-quality labeled tables, we build several strong baselines using state-of-the-art models with deep neural networks."

The use of artificial intelligence (AI) and machine learning is a relatively new concept in the world of investment, but it is one that is increasingly gaining traction and its popularity is only set to growth along with technological advances. In a recent report, Thematic Investing: Transforming World, Bank of America Merrill Lynch identified big data and AI as one of the five key themes set to shape our world in the next five years, with areas such as technology, e-commerce and payments seeing particularly strong tailwinds. "The pace at which themes are transforming businesses is unprecedented today, but we believe this rate of technological change will be faster still over the next five years," the report said. "The exponential growth of data (doubling every two-three years), cheapening computing power …and rise of a connected world (Internet of Things, mobile devices, social media) will bring about the fastest transformation in human history." While some industries are already fully embracing artificial intelligence, we are only just beginning to see the impact it can have on financial services.

Deep learning is at the core of recent spoken language understanding (SLU) related tasks. More precisely, deep neural networks (DNNs) drastically increased the performances of SLU systems, and numerous architectures have been proposed. In the real-life context of theme identification of telephone conversations, it is common to hold both a human, manual (TRS) and an automatically transcribed (ASR) versions of the conversations. Nonetheless, and due to production constraints, only the ASR transcripts are considered to build automatic classifiers. TRS transcripts are only used to measure the performances of ASR systems. Moreover, the recent performances in term of classification accuracy, obtained by DNN related systems are close to the performances reached by humans, and it becomes difficult to further increase the performances by only considering the ASR transcripts. This paper proposes to distillates the TRS knowledge available during the training phase within the ASR representation, by using a new generative adversarial network called M2H-GAN to generate a TRS-like version of an ASR document, to improve the theme identification performances.