Simultaneous Localization and Mapping (SLAM) plays an important role in outdoor and indoor applications ranging from autonomous driving to indoor robotics. Outdoor SLAM has been widely used with the assistance of LiDAR or GPS. For indoor applications, the LiDAR technique does not satisfy the accuracy requirement and the GPS signals will be lost. An accurate and efficient scene sensing technique is required for indoor SLAM. As the most promising 3D sensing technique, the opportunities for indoor SLAM with fringe projection profilometry (FPP) systems are obvious, but methods to date have not fully leveraged the accuracy and speed of sensing that such systems offer. In this paper, we propose a novel FPP-based indoor SLAM method based on the coordinate transformation relationship of FPP, where the 2D-to-3D descriptor-assisted is used for mapping and localization. The correspondences generated by matching descriptors are used for fast and accurate mapping, and the transform estimation between the 2D and 3D descriptors is used to localize the sensor. The provided experimental results demonstrate that the proposed indoor SLAM can achieve the localization and mapping accuracy around one millimeter.

Initial work on variational autoencoders assumed independent latent variables with simple distributions. Subsequent work has explored incorporating more complex distributions and dependency structures: including normalizing flows in the encoder network allows latent variables to entangle non-linearly, creating a richer class of distributions for the approximate posterior, and stacking layers of latent variables allows more complex priors to be specified for the generative model. This work explores incorporating arbitrary dependency structures, as specified by Bayesian networks, into VAEs. This is achieved by extending both the prior and inference network with graphical residual flows - residual flows that encode conditional independence by masking the weight matrices of the flow's residual blocks. We compare our model's performance on several synthetic datasets and show its potential in data-sparse settings.

Organized by Divine Fuh, HUMA – Institute for Humanities in Africa at the University of Cape Town, South Africa and Fanny Chabrol, CEPED-IRD, France and funded by Carnegie Corporation of New York and the Wenner-Gren Foundation, this workshop is located within the framework of the project Future Hospitals: 4IR/AI and the Ethics of Care at HUMA – Institute for Humanities in Africa headed by Divine Fuh, and the "Hospital Multiple" at CEPED-IRD headed by Fanny Chabrol. The workshop aims at proposing new ethnographic methodological and conceptual tools to think and imagine the "hospital of the future" in Africa, in particular, the way artificial intelligence (AI) seeks to transform and is currently transforming access to health care in hospitals today and in the coming years. Our project aims to build a problematisation of the hospital of the future and an ethnographic method to critically analyse the ethical, regulatory, and political issues with respect to AI, healthcare, and hospitals on the continent. We consider the "hospital of the future" – through the digitalization and computer automation of healthcare – as a global promise that needs to be challenged by ethnographic methods within hospitals, engaging with persons interacting with them. The first line of inquiry will challenge the logic of adoption and Africa as a place where development policies are implemented, where infrastructure projects are developed, in which technological innovation, mainly coming from the West, is presented as the promise of better health for those in need.

The global data and intelligence solutions provider, Provenir, is leading the marketplace through its data insights innovation and technologies. The US-based software technology company which supports the international fintech industry, ensures the marketplace is a global data and intelligence ecosystem that makes accessing data fast and easy. Now, Provenir has invited industry professionals to join them in their latest webinar that outline how can AI-powered risk decisioning can play a part in transforming the entire credit risk decisioning process. The session, which is presented by key industry leaders, explores how technology continues to evolve and advances in big data, digital transformation, and AI/ML are creating new opportunities for financial services and fintechs to improve their credit decisioning processes. The webinar panel discussion is being moderated by FinTech Magazine and will provide a spectrum of topics for discussion that outline the importance of using AI/ML to transform credit risk decisioning.

For a long time, it was an accepted truth that video games just didn't work on screen. Remember the quasi-cyberpunk 1993 Super Mario movie, starring Dennis Hopper? It was so bad that basically everyone involved with it has disavowed it. And TV? Kids of the 90s will remember the incredibly annoying voice of Sonic the Hedgehog on Saturday morning TV – or the permanent repeats of the Pokémon anime series – but other than that, the entertainment world never took games seriously. Now, though, things are different.

Healthy Soil Biomes (HSB) and StoryFile have launched an AI-powered conversational video experience with five experts to provide the public with engaging resources on how to create healthy farming methods. Just in time for Earth Day, these methods can help mitigate global issues like food/water scarcity and climate change. For the first time, StoryFile has networked multiple people's StoryFiles and utilized its powerful AI-tool, Conversa, to let users have a conversation that can move between five Healthy Soil Biomes experts according to their area of expertise in areas such as bioreactors, farming, soil, and biodiversity. This revolutionary technology is the basis of Video 3.0, which allows for interactive asynchronous conversational video. Over 7,000 questions were asked of the Healthy Soil Biomes experts.

Parameterized Quantum Circuits (PQC) are drawing increasing research interest thanks to its potential to achieve quantum advantages on near-term Noisy Intermediate Scale Quantum (NISQ) hardware. In order to achieve scalable PQC learning, the training process needs to be offloaded to real quantum machines instead of using exponential-cost classical simulators. One common approach to obtain PQC gradients is parameter shift whose cost scales linearly with the number of qubits. We present QOC, the first experimental demonstration of practical on-chip PQC training with parameter shift. Nevertheless, we find that due to the significant quantum errors (noises) on real machines, gradients obtained from naive parameter shift have low fidelity and thus degrading the training accuracy. To this end, we further propose probabilistic gradient pruning to firstly identify gradients with potentially large errors and then remove them. Specifically, small gradients have larger relative errors than large ones, thus having a higher probability to be pruned. We perform extensive experiments with the Quantum Neural Network (QNN) benchmarks on 5 classification tasks using 5 real quantum machines. The results demonstrate that our on-chip training achieves over 90% and 60% accuracy for 2-class and 4-class image classification tasks. The probabilistic gradient pruning brings up to 7% PQC accuracy improvements over no pruning. Overall, we successfully obtain similar on-chip training accuracy compared with noise-free simulation but have much better training scalability. The QOC code is available in the TorchQuantum library.

Lucas Rizzotto, a YouTuber from Brazil, had no idea what to expect when he gave his Alexa powered smart microwave a brain transplant, replacing the Amazon... 21.04.2022, What he created is a frightening abomination of a poet with an affinity towards Hitler, the British crown and the ending of what it calls the parasitic American empire. Oh, and it wants to kill its creator. There is that, too.Rizzotto, who makes humorous videos about technology projects he builds, used an imaginary friend he had as a child who happened to be embodied in his family's microwave as inspiration. "Magnetron" was a turn-of-the-century British poet who served in World War I, lost his family to the war and later became an expert StarCraft player.

TOKYO -- Zipline, an American company that specializes in using autonomously flying drones to deliver medical supplies, has taken off in Japan. Other parts of Japan may follow, including urban areas, although the biggest needs tend to be in isolated rural areas. Zipline, founded six years ago, already is in service in the U.S., where it has partnered with Walmart Inc. to deliver other products at the retail chain as well as drugs. It is also delivering medical goods in Ghana and Rwanda. Its takeoff in Japan is in partnership with Toyota Tsusho, a group company of Japan's top automaker Toyota Motor Corp. "You can totally transform the way that you react to pandemics, treat patients and do things like home health care delivery," Zipline Chief Executive Keller Rinaudo told The Associated Press.

Six years ago, Rwanda had a blood delivery problem. More than 12 million people live in the small East African country, and like those in other nations, sometimes they get into car accidents. Anemic children need urgent transfusions. You can't predict these emergencies. And when they do, the red stuff stored in Place A has to find its way to a patient in Place B--fast.