Deep neural networks have shown great potential in image reconstruction problems in Euclidean space. However, many reconstruction problems involve imaging physics that are dependent on the underlying non-Euclidean geometry. In this paper, we present a new approach to learn inverse imaging that exploit the underlying geometry and physics. We first introduce a non-Euclidean encoding-decoding network that allows us to describe the unknown and measurement variables over their respective geometrical domains. We then learn the geometry-dependent physics in between the two domains by explicitly modeling it via a bipartite graph over the graphical embedding of the two geometry. We applied the presented network to reconstructing electrical activity on the heart surface from body-surface potential. In a series of generalization tasks with increasing difficulty, we demonstrated the improved ability of the presented network to generalize across geometrical changes underlying the data in comparison to its Euclidean alternatives.

Advances in hardware technology have facilitated more integration of sophisticated software toward augmenting the development of Unmanned Vehicles (UVs) and mitigating constraints for onboard intelligence. As a result, UVs can operate in complex missions where continuous trans-formation in environmental condition calls for a higher level of situational responsiveness and autonomous decision making. This book is a research monograph that aims to provide a comprehensive survey of UVs autonomy and its related properties in internal and external situation awareness to-ward robust mission planning in severe conditions. An advance level of intelligence is essential to minimize the reliance on the human supervisor, which is a main concept of autonomy. A self-controlled system needs a robust mission management strategy to push the boundaries towards autonomous structures, and the UV should be aware of its internal state and capabilities to assess whether current mission goal is achievable or find an alternative solution. In this book, the AUVs will become the major case study thread but other cases/types of vehicle will also be considered. In-deed the research monograph, the review chapters and the new approaches we have developed would be appropriate for use as a reference in upper years or postgraduate degrees for its coverage of literature and algorithms relating to Robot/Vehicle planning, tasking, routing, and trust.

This paper firstly considers the research problem of fairness in collaborative deep learning, while ensuring privacy. A novel reputation system is proposed through digital tokens and local credibility to ensure fairness, in combination with differential privacy to guarantee privacy. In particular, we build a fair and differentially private decentralised deep learning framework called FDPDDL, which enables parties to derive more accurate local models in a fair and private manner by using our developed two-stage scheme: during the initialisation stage, artificial samples generated by Differentially Private Generative Adversarial Network (DPGAN) are used to mutually benchmark the local credibility of each party and generate initial tokens; during the update stage, Differentially Private SGD (DPSGD) is used to facilitate collaborative privacy-preserving deep learning, and local credibility and tokens of each party are updated according to the quality and quantity of individually released gradients. Experimental results on benchmark datasets under three realistic settings demonstrate that FDPDDL achieves high fairness, yields comparable accuracy to the centralised and distributed frameworks, and delivers better accuracy than the standalone framework.

Ready to get started with machine learning (ML) on AWS? ML requires a lot of processing capability, more than you're likely to have at home. That's where a cloud platform such as AWS can help. But how do you get started? Here are some tips to add ML to your career. First, learn as much as you can about ML independent of AWS.

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Google Cloud, has recently launched their AI Adoption Framework whitepaper, authored by Donna Schut, Khalid Salama, Finn Toner, Barbara Fusinska, Valentine Fontama, and Lak Lakshmanan, to provide a guiding framework for enterprises to leverage the power of AI effectively. Google Cloud's AI Adoption Framework has been designed on four pillars of an organisation -- "people, process, technology, and data." Google Cloud blog further noted that these four pillars of organisations should follow six critical themes -- "learn, lead, access, scale, automate, and secure -- for their AI success. According to Google, "These themes are foundational to the AI adoption framework." The first step concerns the scale of'learning' within an organisation, which includes the process of upskilling existing employees, recruiting new talents, and augmenting analytics and engineering professionals with "experience partners." This process of learning will help organisations to decide which analytics and machine learning skills would be required for the business, and accordingly, they can strategies their hiring process amid this crisis. Second, comes the'leading' which concerns whether or not the leaders of organisations provide enough support and guidance to data scientists and engineers to deploy machine learning and artificial intelligence in their business projects. This step would help businesses in understanding the structure of the team, the cost of the projects and the governance of the projects to encourage cross-functional collaboration in the organisation. Next is the'access" to data, where companies recognise the data management strategies and analytics professionals are able to collect, share, discover, analyse the data and other ML artefacts.

The e-prop learning method developed at Graz University of Technology forms the basis for drastically more energy-efficient hardware implementations of Artificial Intelligence. The high energy consumption of artificial neural networks' learning activities is one of the biggest hurdles for the broad use of Artificial Intelligence (AI), especially in mobile applications. One approach to solving this problem can be gleaned from knowledge about the human brain. Although it has the computing power of a supercomputer, it only needs 20 watts, which is only a millionth of the energy of a supercomputer. One of the reasons for this is the efficient transfer of information between neurons in the brain.

Yeshimabeit Milner was in high school the first time she saw kids she knew getting handcuffed and stuffed into police cars. It was February 29, 2008, and the principal of a nearby school in Miami, with a majority Haitian and African-American population, had put one of his students in a chokehold. The next day several dozen kids staged a peaceful demonstration. That night, Miami's NBC 6 News at Six kicked off with a segment called "Chaos on Campus." Cut to blurry phone footage of screaming teenagers: "The chaos you see is an all-out brawl inside the school's cafeteria." Students told reporters that police hit them with batons, threw them on the floor, and pushed them up against walls. The police claimed they were the ones getting attacked--"with water bottles, soda pops, milk, and so on"--and called for emergency backup. Around 25 students were arrested, and many were charged with multiple crimes, including resisting arrest with violence.

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Chatbots are being used increasingly to automate communication. For the most part, this has meant customer service chatbots which use advanced technology -- like AI-natural language processing -- to intelligently answer customer requests. More recently, businesses have started investigating how chatbots may be useful within an organization. Chatbots, which have access to vast knowledge stores and powerful language processing technology, may also be useful in training employees.. Here are four different ways that chatbots can help people build skills.