We first discuss the broader impact of the proposed DynamicD inSec. D presents the training dynamics for the further analysis. E also conducts qualitative experiments to verify whether our approach memorizes the real images for extremely limited data. F shows the hyper-parameter analysis. It demonstrates the importance of discriminator in the two-player competition as simply adjusting the capacity could lead tosuch significant improvements on avarietyof settings, making training generative models more accessible to everyone.

We perform a set of flow and reactive transport simulations within three-dimensional fracture networks to learn the factors controlling mineral reactions. CO$_2$ mineralization requires CO$_2$-laden water, dissolution of a mineral that then leads to precipitation of a CO$_2$-bearing mineral. Our discrete fracture networks (DFN) are partially filled with quartz that gradually dissolves until it reaches a quasi-steady state. At the end of the simulation, we measure the quartz remaining in each fracture within the domain. We observe that a small backbone of fracture exists, where the quartz is fully dissolved which leads to increased flow and transport. However, depending on the DFN topology and the rate of dissolution, we observe a large variability of these changes, which indicates an interplay between the fracture network structure and the impact of geochemical dissolution. In this work, we developed a machine learning framework to extract the important features that support mineralization in the form of dissolution. In addition, we use structural and topological features of the fracture network to predict the remaining quartz volume in quasi-steady state conditions. As a first step to characterizing carbon mineralization, we study dissolution with this framework. We studied a variety of reaction and fracture parameters and their impact on the dissolution of quartz in fracture networks. We found that the dissolution reaction rate constant of quartz and the distance to the flowing backbone in the fracture network are the two most important features that control the amount of quartz left in the system. For the first time, we use a combination of a finite-volume reservoir model and graph-based approach to study reactive transport in a complex fracture network to determine the key features that control dissolution.

Recent advances in Generative Adversarial Networks GANs applications continue to attract the attention of researchers in different fields. In such a framework, two neural networks compete adversely to generate new visual contents indistinguishable from the original dataset. The objective of this research is to create a complementary codesign process between humans and machines to augment character designers abilities in visualizing and creating new characters for multimedia projects such as games and animation. Driven by design cognitive scaffolding, the proposed approach aims to inform the process of perceiving, knowing, and making. The machine generated concepts are used as a launching platform for character designers to conceptualize new characters. A labelled dataset of 22,000 characters was developed for this work and deployed using different GANs to evaluate the most suited for the context, followed by mixed methods evaluation for the machine output and human derivations. The discussed results substantiate the value of the proposed cocreation framework and elucidate how the generated concepts are used as cognitive substances that interact with designers competencies in a versatile manner to influence the creative processes of conceptualizing novel characters.

Face image synthesis has progressed beyond the point at which humans can effectively distinguish authentic faces from synthetically generated ones. Recently developed synthetic face image detectors boast "better-than-human" discriminative ability, especially those guided by human perceptual intelligence during the model's training process. In this paper, we investigate whether these human-guided synthetic face detectors can assist non-expert human operators in the task of synthetic image detection when compared to models trained without human-guidance. We conducted a large-scale experiment with more than 1,560 subjects classifying whether an image shows an authentic or synthetically-generated face, and annotate regions that supported their decisions. In total, 56,015 annotations across 3,780 unique face images were collected. All subjects first examined samples without any AI support, followed by samples given (a) the AI's decision ("synthetic" or "authentic"), (b) class activation maps illustrating where the model deems salient for its decision, or (c) both the AI's decision and AI's saliency map. Synthetic faces were generated with six modern Generative Adversarial Networks. Interesting observations from this experiment include: (1) models trained with human-guidance offer better support to human examination of face images when compared to models trained traditionally using cross-entropy loss, (2) binary decisions presented to humans offers better support than saliency maps, (3) understanding the AI's accuracy helps humans to increase trust in a given model and thus increase their overall accuracy. This work demonstrates that although humans supported by machines achieve better-than-random accuracy of synthetic face detection, the ways of supplying humans with AI support and of building trust are key factors determining high effectiveness of the human-AI tandem.

Artificial intelligence and robotics are disrupting every aspect of work and redefining productivity. The old ways of not just working, but also assessing capabilities, hiring and compensation, are undergoing a massive change. In a conversation with Knowledge@Wharton, Srikanth Karra, chief human resource officer at Indian IT services firm Mphasis, discusses what this means for individuals, organizations and countries. Karra said managerial jobs and tasks that are repetitive in nature will be displaced and the ability to learn new skills will be critical for individuals who want to stay relevant. Companies will need to devise new ways of training and assessing the skills of employees while countries must develop a learning ecosystem. "Work will be more contractual in nature and deep technical skills, creativity and learnability will be at a premium," he noted.

NASA may have equipped its Mars Curiosity rover with an impressive array of scientific instruments, but the robot attaché's size and $2.5-billion price tag give its operators ample reason to steer clear of terrain that could jeopardize its mission. Which is a shame, because much of Mars' craggy, cave-ridden, boulder-strewn landscape is so treacherous (planetary geologists literally call it chaos terrain), that big, expensive robots like Curiosity can't risk accessing it. That's why NASA's Jet Propulsion Laboratory built Puffer. Short for Pop-Up Flat Folding Explorer Robot, Puffer is the agency's latest origami-inspired device. JPL has experimented previously with collapsible solar panels based on the Japanese art of folding paper.

The great explorers Lewis and Clark knew the importance of team expeditions. Now, engineers at NASA's Jet Propulsion Laboratory in Pasadena, California are developing a small scout robot called the Pop-Up Flat Folding Explorer Robot (PUFFER) to accompany the next generation of Martian rovers in their outer space explorations. Inspired by origami, the Japanese art of paper folding, PUFFER is designed to change shape in order to squeeze into small crevasses that are too tight for rovers to reach. So far the two-wheeled scout has been successfully tested in hostile and diverse terrains including the Mojave Desert and Antartica. Though rovers themselves are built to last, they're expensive and NASA engineers take care not to send them on overtly dangerous missions.

You're so hard to explore. Sometimes you bombard spacecrafts with hurtling rocks and deadly cosmic rays, and other times you're so empty you don't give astronauts a darn thing to hold on to. But while scientists haven't quite figured out how to keep radiation at bay, the scientists at NASA's Jet Propulsion Laboratory--specifically, its Planetary Robotics Laboratory--are building machines that can get a grip on the most difficult surfaces astronauts will find out there. Adhesion-wise, space presents a couple problems. First, robots typically struggle with uneven surfaces, let alone the kind of cliffs and crags you see on Mars.