In situations where the solution of a high-fidelity dynamical system needs to be evaluated repeatedly, over a vast pool of parametric configurations and in absence of access to the underlying governing equations, data-driven model reduction techniques are preferable. We propose a novel active learning approach to build a parametric data-driven reduced-order model (ROM) by greedily picking the most important parameter samples from the parameter domain. As a result, during the ROM construction phase, the number of high-fidelity solutions dynamically grow in a principled fashion. The high-fidelity solution snapshots are expressed in several parameter-specific linear subspaces, with the help of proper orthogonal decomposition (POD), and the relative distance between these subspaces is used as a guiding mechanism to perform active learning. For successfully achieving this, we provide a distance measure to evaluate the similarity between pairs of linear subspaces with different dimensions, and also show that this distance measure is a metric. The usability of the proposed subspace-distance-enabled active learning (SDE-AL) framework is demonstrated by augmenting two existing non-intrusive reduced-order modeling approaches, and providing their active-learning-driven (ActLearn) extensions, namely, SDE-ActLearn-POD-KSNN, and SDE-ActLearn-POD-NN. Furthermore, we report positive results for two parametric physical models, highlighting the efficiency of the proposed SDE-AL approach.

It was some time in the early 2000s and Asif Kapadia, already a successful film director, a wunderkind whose first feature in 2001, The Warrior, won the Bafta for outstanding British film, was travelling back from New York. I'm in a limo being taken to the airport. And I was taking photos of Manhattan because I was driving over Brooklyn Bridge and it's just all so cinematic and I became subconsciously aware of the driver watching me in the rear view mirror. "I get to the airport and I'm in the Virgin lounge when my name is called out. And I thought: 'Have I left a bag or something?' But then five or six people come: homeland security. And they stop me in the lounge in front of everyone, the only person of colour in there, and empty out my bag, and they say: 'Someone's reported you.' And it's like: 'Who are you? An itinerary of his trip and its purpose proved his credentials and he was eventually allowed to go and boarded his flight. But for nearly a decade afterwards, he found himself on a "watch list". "I would get stopped and interviewed two times before I got on a plane, pulled out in a room.

When repeated evaluations for varying parameter configurations of a high-fidelity physical model are required, surrogate modeling techniques based on model order reduction are desired. In absence of the governing equations describing the dynamics, we need to construct the parametric reduced-order surrogate model in a non-intrusive fashion. In this setting, the usual residual-based error estimate for optimal parameter sampling associated with the reduced basis method is not directly available. Our work provides a non-intrusive optimality criterion to efficiently populate the parameter snapshots, thereby, enabling us to effectively construct a parametric surrogate model. We consider separate parameter-specific proper orthogonal decomposition (POD) subspaces and propose an active-learning-driven surrogate model using kernel-based shallow neural networks, abbreviated as ActLearn-POD-KSNN surrogate model. To demonstrate the validity of our proposed ideas, we present numerical experiments using two physical models, namely Burgers' equation and shallow water equations. Both the models have mixed -- convective and diffusive -- effects within their respective parameter domains, with each of them dominating in certain regions. The proposed ActLearn-POD-KSNN surrogate model efficiently predicts the solution at new parameter locations, even for a setting with multiple interacting shock profiles.

In the United States, the average patient sees a physician just three times per year, according to the CDC, for visits lasting just 20 minutes each. Doctors, for their part, put more time into administrative tasks than face-to-face care: every hour spent with patients takes about two hours at the desk for documentation and other tasks, according to a Stanford study. More than half of all doctors report symptoms of burnout. In the United States, the average patient sees a physician just three times per year, for visits lasting just 20 minutes each. Voice-driven artificial intelligence (AI) can help cure the time shortage on both ends of the spectrum.

Artificial intelligence is no longer just a futuristic technology. It is now being applied throughout the healthcare arena from imaging to triaging patients. But outside of the mainstream hospital uses the technology is also be deployed in apps, wearables and trackers. Here is five of the cutting edge ways AI is being used by health professionals today. One problem that innovators are looking to tackle with AI is paperwork.

Wearables aren't just for tracking steps anymore, as a new tool is helping doctors transcribe right from their smartwatch. This morning, voice-powered healthcare company Notable unveiled its latest technology, a voice-powered artificial intelligence wearable for doctors. "The current physician workflow is rife with administrative burdens and complexity. With growing documentation requirements, studies have shown that physicians spend more than half of their day in EHRs and a third of their day with patients," Pranay Kapadia, Notable CEO, said in an email to MobiHealthNews. "With Notable, physicians free up their day to offer better patient care. During appointments, they can be better listeners and more effective educators by maintaining eye contact with patients. Since Notable is passively capturing and charting for them in the background, there's no need to type or take notes during patient time. Between appointments, their charting workflow is dramatically simplified and sped up. After a brief dictation, they just need to review programmatically generated notes, codes, and orders -- then can move on to the next patient."

In order to use computational intelligence for automated narrative synthesis, domain knowledge of the story world must be defined, a task which is currently confined to experts. This paper discusses the benefits and tradeoffs between agent-centric and event-centric approaches towards authoring the domain knowledge of story worlds. In an effort to democratize story world creation, we present an accessible graphical platform for content creators and even end users to create their own story worlds, populate it with smart characters and objects, and define narrative events that can be used by existing tools for automated narrative synthesis. We demonstrate the potential of our system by authoring a simple bank robbery story world, and integrate it with existing solutions for event-centric planning to synthesize example digital stories.